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This paper studies auxiliary trajectory preferences (confidence-based and entropy-based metrics) to improve small language model self-improvement and transfer, showing UID-Conj. gains of about 2 points over unmodified TTRL and uniform-weighted SFT on AIME tasks.
This paper revisits the Uniform Information Density (UID) hypothesis in the context of LLM reasoning, introducing an entropy-based framework to quantify information flow uniformity. Across seven reasoning benchmarks, the authors find that high-quality reasoning exhibits local uniformity in step transitions but global non-uniformity in trajectory structure, suggesting LLM reasoning differs fundamentally from human communication patterns.