Show HN: Entropy-Guided Loop – How to make small models reason
7 days ago
- #AI Reasoning
- #Logprobs
- #Uncertainty Metrics
- Novel approach to improve AI model reasoning using token-level uncertainty metrics (logprobs).
- Comparison between uncertainty-aware approach and traditional reasoning models.
- Implementation of an uncertainty-aware generation loop with refinement passes triggered by high uncertainty.
- Use of OpenAI Responses API with logprobs and Weave for experiment tracking.
- Performance metrics include perplexity, average log probabilities, response accuracy, token usage, and generation time.
- Significant cost reduction (30-43%) compared to reasoning models while maintaining quality.
- Future directions include integrating pre-softmax hidden states, multi-layer uncertainty aggregation, and streaming with real-time monitoring.
- Project status: Active development with benchmark validation in progress.
- Open-source contributions welcome in areas like alternative uncertainty metrics and visualization improvements.