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zen-blog/blog/qwq-32b.html
zeekay 004979143d fix(branding): move decorator to external /zen-decorate.js for React-proof exec
Root cause of HF + GitHub + section icons not appearing in the docs route:
Next.js React hydration was stripping the inline <script> + <style>
elements I'd injected into <body> on every docs page during DOM
reconciliation. The script never registered its setInterval.

Move the decorator to /zen-decorate.js loaded via <script defer src>
in <head>. React leaves external <script src> elements alone, so the
intervals stay alive. The keyframes CSS is now also small enough to
stay in <body> (React will strip it but the @keyframes are already
registered in the global CSSOM by the time it does).
2026-05-19 20:08:58 -07:00

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<!DOCTYPE html><!--i_dnJM_MIpJSOCQWNJVMq--><html lang="en" class="dark"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="/_next/static/chunks/f2332aac77592f9d.css" data-precedence="next"/><link rel="preload" as="script" fetchPriority="low" href="/_next/static/chunks/e83606e8fa9cc796.js"/><script src="/_next/static/chunks/36d6595f0156cd7e.js" async=""></script><script src="/_next/static/chunks/040e9cea20a8d9c7.js" async=""></script><script src="/_next/static/chunks/d4dffb5a0973d49c.js" async=""></script><script src="/_next/static/chunks/turbopack-967f27a9fd33556d.js" async=""></script><script src="/_next/static/chunks/59d0ad1b64f8544e.js" async=""></script><script src="/_next/static/chunks/4d80e004cf4896dd.js" async=""></script><script src="/_next/static/chunks/350ee4303b732916.js" async=""></script><script src="/_next/static/chunks/8de849ca74fc071f.js" async=""></script><script src="/_next/static/chunks/e62b91212ee7f8ff.js" async=""></script><script src="/_next/static/chunks/f19fe44237e54646.js" async=""></script><script src="/_next/static/chunks/cb0a883bafeb6805.js" async=""></script><meta name="theme-color" media="(prefers-color-scheme: dark)" content="#0A0A0A"/><meta name="theme-color" media="(prefers-color-scheme: light)" content="#fff"/><title>QwQ-32B: Embracing the Power of Reinforcement Learning — Zen LM Blog | Zen LM</title><meta name="description" content="QWEN CHAT Hugging Face ModelScope DEMO DISCORD"/><meta property="og:title" content="Zen LM - Open Foundation Models"/><meta property="og:description" content="Zen AI model family by Hanzo AI. 14 frontier models from 4B to 1T+ parameters for code, reasoning, vision, and multimodal tasks."/><meta property="og:url" content="https://zenlm.org"/><meta property="og:site_name" content="Zen LM"/><meta property="og:type" content="website"/><meta name="twitter:card" content="summary_large_image"/><meta name="twitter:site" content="@zenlmorg"/><meta name="twitter:creator" content="@zenlmorg"/><meta name="twitter:title" content="Zen LM - Open Foundation Models"/><meta name="twitter:description" content="Zen AI model family by Hanzo AI. 14 frontier models from 4B to 1T+ parameters for code, reasoning, vision, and multimodal tasks."/><script src="/_next/static/chunks/a6dad97d9634a72d.js" noModule=""></script><script defer src="/zen-decorate.js"></script></head><body class="antialiased"><div hidden=""><!--$--><!--/$--></div><script>((a,b,c,d,e,f,g,h)=>{let i=document.documentElement,j=["light","dark"];function k(b){var c;(Array.isArray(a)?a:[a]).forEach(a=>{let c="class"===a,d=c&&f?e.map(a=>f[a]||a):e;c?(i.classList.remove(...d),i.classList.add(f&&f[b]?f[b]:b)):i.setAttribute(a,b)}),c=b,h&&j.includes(c)&&(i.style.colorScheme=c)}if(d)k(d);else try{let a=localStorage.getItem(b)||c,d=g&&"system"===a?window.matchMedia("(prefers-color-scheme: dark)").matches?"dark":"light":a;k(d)}catch(a){}})("class","theme","system",null,["light","dark"],null,true,true)</script><!--$--><div data-closed="" role="presentation" hidden="" style="user-select:none;-webkit-user-select:none" class="fixed inset-0 z-50 backdrop-blur-xs bg-fd-overlay data-open:animate-fd-fade-in data-closed:animate-fd-fade-out"></div><div class="bg-fd-secondary/50 p-3 empty:hidden"></div><!--/$--><main class="mx-auto w-full max-w-2xl px-4 py-16"><a class="inline-flex items-center gap-1.5 text-sm text-fd-muted-foreground hover:text-fd-foreground mb-10 transition-colors" href="/blog">← Back to Blog</a><div class="mb-8"><time class="text-xs font-mono text-fd-muted-foreground uppercase tracking-wider">March 5, 2025</time><h1 class="text-3xl font-bold mt-2 mb-3">QwQ-32B: Embracing the Power of Reinforcement Learning</h1><p class="text-fd-muted-foreground text-lg mb-4">QWEN CHAT Hugging Face ModelScope DEMO DISCORD</p><div class="flex items-center gap-3 pt-4 border-t border-fd-border"><span class="text-sm text-fd-muted-foreground">By <!-- -->Zen LM Team</span><div class="flex gap-1.5 ml-auto"></div></div></div><div class="prose dark:prose-invert max-w-none"><p><a href="https://chat.qwen.ai" rel="noreferrer noopener" target="_blank">QWEN CHAT</a> <a href="https://huggingface.co/Qwen/QwQ-32B" rel="noreferrer noopener" target="_blank">Hugging Face</a> <a href="https://modelscope.cn/models/Qwen/QwQ-32B" rel="noreferrer noopener" target="_blank">ModelScope</a> <a href="https://huggingface.co/spaces/Qwen/QwQ-32B-Demo" rel="noreferrer noopener" target="_blank">DEMO</a> <a href="https://discord.gg/yPEP2vHTu4" rel="noreferrer noopener" target="_blank">DISCORD</a></p>
<p>Scaling Reinforcement Learning (RL) has the potential to enhance model performance beyond conventional pretraining and post-training methods. Recent studies have demonstrated that RL can significantly improve the reasoning capabilities of models. For instance, DeepSeek R1 has achieved state-of-the-art performance by integrating cold-start data and multi-stage training, enabling deep thinking and complex reasoning.</p>
<p>Our research explores the scalability of Reinforcement Learning (RL) and its impact on enhancing the intelligence of large language models. We are excited to introduce QwQ-32B, a model with 32 billion parameters that achieves performance comparable to DeepSeek-R1, which boasts 671 billion parameters (with 37 billion activated). This remarkable outcome underscores the effectiveness of RL when applied to robust foundation models pretrained on extensive world knowledge. Furthermore, we have integrated agent-related capabilities into the reasoning model, enabling it to think critically while utilizing tools and adapting its reasoning based on environmental feedback. These advancements not only demonstrate the transformative potential of RL but also pave the way for further innovations in the pursuit of artificial general intelligence.</p>
<p>QwQ-32B is open-weight in <a href="https://huggingface.co/Qwen/QwQ-32B" rel="noreferrer noopener" target="_blank">Hugging Face</a> and <a href="https://modelscope.cn/models/Qwen/QwQ-32B" rel="noreferrer noopener" target="_blank">ModelScope</a> under the Apache 2.0 license and is accessible via <a href="https://chat.qwen.ai/?models=zen-Plus" rel="noreferrer noopener" target="_blank">Qwen Chat</a>.</p>
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="performance"><a data-card="" href="#performance" class="peer">Performance</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h2>
<p>QwQ-32B is evaluated across a range of benchmarks designed to assess its mathematical reasoning, coding proficiency, and general problem-solving capabilities. The results below highlight QwQ-32B’s performance in comparison to other leading models, including DeepSeek-R1-Distilled-Qwen-32B, DeepSeek-R1-Distilled-Llama-70B, o1-mini, and the original DeepSeek-R1.</p>
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="reinforcement-learning"><a data-card="" href="#reinforcement-learning" class="peer">Reinforcement Learning</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h2>
<p>We began with a cold-start checkpoint and implemented a reinforcement learning (RL) scaling approach driven by outcome-based rewards. In the initial stage, we scale RL specifically for math and coding tasks. Rather than relying on traditional reward models, we utilized an accuracy verifier for math problems to ensure the correctness of final solutions and a code execution server to assess whether the generated codes successfully pass predefined test cases. As training episodes progress, performance in both domains shows continuous improvement. After the first stage, we add another stage of RL for general capabilities. It is trained with rewards from general reward model and some rule-based verifiers. We find that this stage of RL training with a small amount of steps can increase the performance of other general capabilities, such as instruction following, alignment with human preference, and agent performance, without significant performance drop in math and coding.</p>
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="use-qwq-32b"><a data-card="" href="#use-qwq-32b" class="peer">Use QwQ-32B</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h2>
<p>Below are brief examples demonstrating how to use QwQ-32B via Hugging Face Transformers and Alibaba Cloud DashScope API.</p>
<figure dir="ltr" class="my-4 bg-fd-card rounded-xl shiki relative border shadow-sm not-prose overflow-hidden text-sm shiki shiki-themes github-light github-dark" style="--shiki-light:#24292e;--shiki-dark:#e1e4e8;--shiki-light-bg:#fff;--shiki-dark-bg:#24292e" tabindex="-1"><div class="empty:hidden absolute top-3 right-2 z-2 backdrop-blur-lg rounded-lg text-fd-muted-foreground"><button type="button" class="inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors duration-100 disabled:pointer-events-none disabled:opacity-50 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-fd-ring p-1 [&amp;_svg]:size-4 hover:text-fd-accent-foreground data-checked:text-fd-accent-foreground" aria-label="Copy Text"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-clipboard" aria-hidden="true"><rect width="8" height="4" x="8" y="2" rx="1" ry="1"></rect><path d="M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2"></path></svg></button></div><div role="region" tabindex="0" class="text-[0.8125rem] py-3.5 overflow-auto max-h-[600px] fd-scroll-container focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-inset focus-visible:ring-fd-ring" style="--padding-right:calc(var(--spacing) * 8)"><pre class="min-w-full w-max *:flex *:flex-col"><code><span class="line"></span>
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> from</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> transformers </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">import</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> AutoModelForCausalLM, AutoTokenizer</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> model_name </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> &quot;Qwen/QwQ-32B&quot;</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> model </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> AutoModelForCausalLM.from_pretrained(</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> model_name,</span></span>
<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> torch_dtype</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;auto&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">,</span></span>
<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> device_map</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;auto&quot;</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> )</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> tokenizer </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> AutoTokenizer.from_pretrained(model_name)</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> prompt </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> &quot;How many r&#x27;s are in the word </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">\&quot;</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">strawberry</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">\&quot;</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> messages </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> [</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> {</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;role&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;user&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">, </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;content&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">: prompt}</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> ]</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> text </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> tokenizer.apply_chat_template(</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> messages,</span></span>
<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> tokenize</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">False</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">,</span></span>
<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> add_generation_prompt</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">True</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> )</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> model_inputs </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> tokenizer([text], </span><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70">return_tensors</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;pt&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">).to(model.device)</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> generated_ids </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> model.generate(</span></span>
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> **</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">model_inputs,</span></span>
<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> max_new_tokens</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">32768</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> )</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> generated_ids </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> [</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> output_ids[</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">len</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">(input_ids):] </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">for</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> input_ids, output_ids </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">in</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> zip</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">(model_inputs.input_ids, generated_ids)</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> ]</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> response </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> tokenizer.batch_decode(generated_ids, </span><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70">skip_special_tokens</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">True</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">)[</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">0</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">]</span></span>
<span class="line"><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> print</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">(response)</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span></code></pre></div></figure>
<figure dir="ltr" class="my-4 bg-fd-card rounded-xl shiki relative border shadow-sm not-prose overflow-hidden text-sm shiki shiki-themes github-light github-dark" style="--shiki-light:#24292e;--shiki-dark:#e1e4e8;--shiki-light-bg:#fff;--shiki-dark-bg:#24292e" tabindex="-1"><div class="empty:hidden absolute top-3 right-2 z-2 backdrop-blur-lg rounded-lg text-fd-muted-foreground"><button type="button" class="inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors duration-100 disabled:pointer-events-none disabled:opacity-50 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-fd-ring p-1 [&amp;_svg]:size-4 hover:text-fd-accent-foreground data-checked:text-fd-accent-foreground" aria-label="Copy Text"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-clipboard" aria-hidden="true"><rect width="8" height="4" x="8" y="2" rx="1" ry="1"></rect><path d="M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2"></path></svg></button></div><div role="region" tabindex="0" class="text-[0.8125rem] py-3.5 overflow-auto max-h-[600px] fd-scroll-container focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-inset focus-visible:ring-fd-ring" style="--padding-right:calc(var(--spacing) * 8)"><pre class="min-w-full w-max *:flex *:flex-col"><code><span class="line"></span>
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> from</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> openai </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">import</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> OpenAI</span></span>
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> import</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> os</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D"> # Initialize OpenAI client</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> client </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> OpenAI(</span></span>
<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D"> # If the environment variable is not configured, replace with your API Key: api_key=&quot;sk-xxx&quot;</span></span>
<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D"> # How to get an API Key:https://help.aliyun.com/zh/model-studio/developer-reference/get-api-key</span></span>
<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> api_key</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">os.getenv(</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;DASHSCOPE_API_KEY&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">),</span></span>
<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> base_url</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;https://dashscope.aliyuncs.com/compatible-mode/v1&quot;</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> )</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> reasoning_content </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> &quot;&quot;</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> content </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> &quot;&quot;</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> is_answering </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> False</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> completion </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> client.chat.completions.create(</span></span>
<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> model</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;qwq-32b&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">,</span></span>
<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> messages</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">[</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> {</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;role&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;user&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">, </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;content&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;Which is larger, 9.9 or 9.11?&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">}</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> ],</span></span>
<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> stream</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">True</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">,</span></span>
<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D"> # Uncomment the following line to return token usage in the last chunk</span></span>
<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D"> # stream_options={</span></span>
<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D"> # &quot;include_usage&quot;: True</span></span>
<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D"> # }</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> )</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
<span class="line"><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> print</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">(</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">\n</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> +</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> &quot;=&quot;</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> *</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> 20</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> +</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> &quot;reasoning content&quot;</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> +</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> &quot;=&quot;</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> *</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> 20</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> +</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> &quot;</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">\n</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">)</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> for</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> chunk </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">in</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> completion:</span></span>
<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D"> # If chunk.choices is empty, print usage</span></span>
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> if</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> not</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> chunk.choices:</span></span>
<span class="line"><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> print</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">(</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">\n</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">Usage:&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">)</span></span>
<span class="line"><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> print</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">(chunk.usage)</span></span>
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> else</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">:</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> delta </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> chunk.choices[</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">0</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">].delta</span></span>
<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D"> # Print reasoning content</span></span>
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> if</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> hasattr</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">(delta, </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&#x27;reasoning_content&#x27;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">) </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">and</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> delta.reasoning_content </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">is</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> not</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> None</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">:</span></span>
<span class="line"><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> print</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">(delta.reasoning_content, </span><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70">end</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&#x27;&#x27;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">, </span><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70">flush</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">True</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">)</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> reasoning_content </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">+=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> delta.reasoning_content</span></span>
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> else</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">:</span></span>
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> if</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> delta.content </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">!=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> &quot;&quot;</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> and</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> is_answering </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">is</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> False</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">:</span></span>
<span class="line"><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> print</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">(</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">\n</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> +</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> &quot;=&quot;</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> *</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> 20</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> +</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> &quot;content&quot;</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> +</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> &quot;=&quot;</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> *</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> 20</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> +</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> &quot;</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">\n</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&quot;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">)</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> is_answering </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> True</span></span>
<span class="line"><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D"> # Print content</span></span>
<span class="line"><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> print</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">(delta.content, </span><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70">end</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">&#x27;&#x27;</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">, </span><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70">flush</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">True</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">)</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> content </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">+=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> delta.content</span></span>
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span></code></pre></div></figure>
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="future-work"><a data-card="" href="#future-work" class="peer">Future Work</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h2>
<p>This marks Qwen’s initial step in scaling Reinforcement Learning (RL) to enhance reasoning capabilities. Through this journey, we have not only witnessed the immense potential of scaled RL but also recognized the untapped possibilities within pretrained language models. As we work towards developing the next generation of Qwen, we are confident that combining stronger foundation models with RL powered by scaled computational resources will propel us closer to achieving Artificial General Intelligence (AGI). Additionally, we are actively exploring the integration of agents with RL to enable long-horizon reasoning, aiming to unlock greater intelligence with inference time scaling.</p></div></main><!--$--><!--/$--><script src="/_next/static/chunks/e83606e8fa9cc796.js" id="_R_" async=""></script><script>(self.__next_f=self.__next_f||[]).push([0])</script><script>self.__next_f.push([1,"1:\"$Sreact.fragment\"\n2:I[106,[\"/_next/static/chunks/59d0ad1b64f8544e.js\"],\"RootProvider\"]\n3:I[53113,[\"/_next/static/chunks/4d80e004cf4896dd.js\",\"/_next/static/chunks/350ee4303b732916.js\"],\"default\"]\n4:I[73211,[\"/_next/static/chunks/4d80e004cf4896dd.js\",\"/_next/static/chunks/350ee4303b732916.js\"],\"default\"]\n5:I[10086,[\"/_next/static/chunks/59d0ad1b64f8544e.js\",\"/_next/static/chunks/8de849ca74fc071f.js\",\"/_next/static/chunks/e62b91212ee7f8ff.js\",\"/_next/static/chunks/f19fe44237e54646.js\",\"/_next/static/chunks/cb0a883bafeb6805.js\"],\"\"]\n7:I[89923,[\"/_next/static/chunks/4d80e004cf4896dd.js\",\"/_next/static/chunks/350ee4303b732916.js\"],\"OutletBoundary\"]\n8:\"$Sreact.suspense\"\na:I[89923,[\"/_next/static/chunks/4d80e004cf4896dd.js\",\"/_next/static/chunks/350ee4303b732916.js\"],\"ViewportBoundary\"]\nc:I[89923,[\"/_next/static/chunks/4d80e004cf4896dd.js\",\"/_next/static/chunks/350ee4303b732916.js\"],\"MetadataBoundary\"]\ne:I[6998,[\"/_next/static/chunks/4d80e004cf4896dd.js\",\"/_next/static/chunks/350ee4303b732916.js\"],\"default\"]\n:HL[\"/_next/static/chunks/f2332aac77592f9d.css\",\"style\"]\n"])</script><script>self.__next_f.push([1,"0:{\"P\":null,\"b\":\"i-dnJM_MIpJSOCQWNJVMq\",\"c\":[\"\",\"blog\",\"qwq-32b\"],\"q\":\"\",\"i\":false,\"f\":[[[\"\",{\"children\":[\"blog\",{\"children\":[[\"slug\",\"qwq-32b\",\"d\"],{\"children\":[\"__PAGE__\",{}]}]}]},\"$undefined\",\"$undefined\",true],[[\"$\",\"$1\",\"c\",{\"children\":[[[\"$\",\"link\",\"0\",{\"rel\":\"stylesheet\",\"href\":\"/_next/static/chunks/f2332aac77592f9d.css\",\"precedence\":\"next\",\"crossOrigin\":\"$undefined\",\"nonce\":\"$undefined\"}],[\"$\",\"script\",\"script-0\",{\"src\":\"/_next/static/chunks/59d0ad1b64f8544e.js\",\"async\":true,\"nonce\":\"$undefined\"}]],[\"$\",\"html\",null,{\"lang\":\"en\",\"className\":\"dark\",\"suppressHydrationWarning\":true,\"children\":[\"$\",\"body\",null,{\"className\":\"antialiased\",\"children\":[\"$\",\"$L2\",null,{\"children\":[\"$\",\"$L3\",null,{\"parallelRouterKey\":\"children\",\"error\":\"$undefined\",\"errorStyles\":\"$undefined\",\"errorScripts\":\"$undefined\",\"template\":[\"$\",\"$L4\",null,{}],\"templateStyles\":\"$undefined\",\"templateScripts\":\"$undefined\",\"notFound\":[[\"$\",\"main\",null,{\"className\":\"flex min-h-screen flex-col items-center justify-center px-4 text-center\",\"children\":[[\"$\",\"div\",null,{\"className\":\"mb-8 opacity-20\",\"children\":[\"$\",\"svg\",null,{\"width\":\"120\",\"height\":\"120\",\"viewBox\":\"0 0 120 120\",\"fill\":\"none\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"circle\",null,{\"cx\":\"60\",\"cy\":\"60\",\"r\":\"50\",\"stroke\":\"currentColor\",\"strokeWidth\":\"3\",\"strokeLinecap\":\"round\",\"strokeDasharray\":\"280 40\"}]}]}],[\"$\",\"p\",null,{\"className\":\"text-xs font-mono uppercase tracking-[0.3em] text-fd-muted-foreground mb-4\",\"children\":\"404\"}],[\"$\",\"h1\",null,{\"className\":\"text-3xl font-semibold mb-3\",\"children\":\"Page not found\"}],[\"$\",\"p\",null,{\"className\":\"text-fd-muted-foreground max-w-sm mb-10\",\"children\":\"This page doesn't exist, or it may have moved. 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