ETR: Entropy Trend Reward for Efficient Chain-of-Thought Reasoning
Chain-of-thought (CoT) reasoning improves large language model performance on complex tasks, but often produces excessively long and inefficient reasoning traces. Existing methods shorten CoTs using length penalties or global entropy reduction, implicitly assuming that low uncertainty is desirable throughout reasoning. We show instead that reasoning efficiency is governed by the trajectory of uncertainty. CoTs with dominant downward entropy trends are substantially shorter. Motivated by this insight, we propose Entropy Trend Reward (ETR), a trajectory-aware objective that encourages progressive uncertainty reduction while allowing limited local exploration. We integrate ETR into Group Rel...
| Authors | Xuan Xiong, Huan Liu, Li Gu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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