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…
Entropy-Guided Loop – How to make small models reason launched on September 3, 2025 with 33 votes, #488 of 8,160 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 1 year on.
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
A notebook that compares a reasoning model x a non reasoning model that runs a loop using logprobs found uncertainty - monostate/weave-logprobs-reasoning-loop
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…
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