On March 22nd, a LocalLLaMA user reported a specific and quietly interesting failure: attempting to prompt a local language model to write code inside its `<think>` reasoning block — the scratchpad that models like DeepSeek-R1 and its derivatives use to work through problems before delivering a final answer — and finding that it simply didn't work. The post is sparse on detail, which is part of why the confidence sits where it does. But the observation touches something real. The `<think>` block was designed as a space for chain-of-thought reasoning, not as an execution or generation environment in its own right, and users have been probing its edges ever since these reasoning models started proliferating through the open-source community. Whether this was a model-specific quirk, a deliberate constraint, or something else entirely remains unestablished.
If confirmed, here is what this means. The boundary between a model's reasoning process and its output is not just a formatting convention — it reflects something architectural about how these models were trained and what behaviors they were rewarded for. If code generation is genuinely suppressed or degraded inside the thinking layer, that has real implications for anyone building agentic systems that try to use chain-of-thought reasoning as a lightweight execution environment, a pattern that's gained traction as developers look for ways to get models to self-verify or self-correct before committing to a final response. It also raises a sharper question about whether the `<think>` block is a window into genuine reasoning or a trained performance of it — and whether those two things can ever be fully separated.
Watch for follow-up posts on LocalLLaMA or Hugging Face forums where users test this across different model variants and quantizations, particularly any response from teams maintaining popular DeepSeek-R1 derivatives. A pattern across multiple models would change the picture considerably.
<!-- SC_OFF --><div class="md"><p>In LM Studio the new Qwen 3.5 models (4b 9b 27b 122b) when analyzing large (more than 50k tokens) texts start to output gibberish. It is not a
<!-- SC_OFF --><div class="md"><p>I build context/harness optimization tooling, so provider-side serialization quirks actually matter to me. If you're optimizing over prompts, you
<!-- SC_OFF --><div class="md"><p>Beginner here, now I have a good GPU and ollama using docker. Pulled the Gemma4 weights and was able to add it to cursor using ngrok. </p>
<!-- SC_OFF --><div class="md"><blockquote> <p>Wait, I need to be careful about the "no_think" tag in the system prompt. The system prompt says <code>/no_think</code>.
<!-- SC_OFF --><div class="md"><p>Built a free alternative to Claude Code ($20-$200/mo) that uses NVIDIA's open models through the same CLI framework (FREE!).</p> <p><strong>How
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s1bjtr/getting_stuck_in_loops_w_tool_calls/"> <img alt="Getting Stuck in Loops w Tool Calls"
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s3bxn2/cant_get_continue_to_go_through_the_code_instead/"> <img alt="Can't get Continue to go through the
<!-- SC_OFF --><div class="md"><p>Built a local ReAct-style calculator agent with 6 tools:</p> <ul> <li>add</li> <li>subtract</li> <li>multiply</li> <li>divide</li>
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