Entropy-Guided Loop – How to make small models reason
TLDR: A small, vendor-agnostic inference loop that turns token logprobs/perplexity/entropy into an extra pass and reasoning for LLMs. - Captures logprobs/top-k during generation, computes perplexity and token-level entropy. - Triggers at most one refine when simple thresholds fire; passes a compact “uncertainty report” (uncertain tokens + top-k alts + local context) back to the model. - In our tests on technical Q&A / math / code, a small model recovered much of “reasoning” quality at ~⅓ the cost while refining ~⅓ of outputs. I kept seeing “reasoning” models behave…
In plain words
Entropy-Guided Loop is an open-source inference system that monitors token uncertainty during language model generation and triggers selective refinement passes when confidence drops below configurable thresholds. It captures log probabilities and entropy signals normally discarded, then passes an uncertainty report back to the model for targeted reconsideration. Designed as vendor-agnostic, it works with small models on technical questions, math, and code tasks, achieving comparable reasoning quality to larger models at roughly one-third the computational cost by refining only uncertain outputs.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
TLDR: A small, vendor-agnostic inference loop that turns token logprobs/perplexity/entropy into an extra pass and reasoning for LLMs. - Captures logprobs/top-k during generation, computes perplexity and token-level entropy. - Triggers at most one refine when simple thresholds fire; passes a compact “uncertainty report” (uncertain tokens + top-k alts + local context) back to the model. - In our tests on technical Q&A / math / code, a small model recovered much of “reasoning” quality at ~⅓ the cost while refining ~⅓ of outputs. I kept seeing “reasoning” models behave like expensive black boxes. Meanwhile, standard inference already computes useful signals both before softmax normalization and after it(logprobs), which we usually throw away. This loop tries the simplest thing that you could think of: use those signals to decide when (and where) to think again. GitHub (notebook + minimal code): https://github.com/monostate/weave-logprobs-reasoning-loop Paper (short & engineer made): https://arxiv.org/abs/2509.00079 Blog (more context): https://monostate.ai/blog/entropy-refinement-blog Requirements: Python, API that exposes logprobs (tested with OpenAI non reasoning 4.1). OPENAI_API_KEY and WEAVE for observability. Run the notebook; it prints metrics and shows which tokens triggered refinement. - Python, simple loop (no retraining). - Uses Responses API logprobs/top-k; metrics: perplexity, max token entropy, low-confidence counts. - Weave for lightweight logging/observability (optional). - Passing alternatives (not just “this looks uncertain”) prevents over-correction. - A simple OR rule (ppl / max-entropy / low-confidence count) catches complementary failure modes. - Numbers drift across vendors; keeping the method vendor-agnostic is better than chasing fragile pairings. - Needs APIs that expose logprobs/top-k. - Results are indicative—not a leaderboard; focus is on within-model gains (single-pass vs +loop). - Thresholds might need light tuning per domain. - One pass only; not a chain-of-thought replacement. - Run it on your models and ideas (e.g., 4o-mini, v3, Llama variants with logprobs) and share logs in a PR for our README in GitHub if you'd like, PRs welcome - I’ll credit and link. Overall let me know if you find making small models reason like this useful!
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