InfoDensity: Rewarding Information-Dense Traces for Efficient Reasoning
Large Language Models (LLMs) with extended reasoning capabilities often generate verbose and redundant reasoning traces, incurring unnecessary computational cost. While existing reinforcement learning approaches address this by optimizing final response length, they neglect the quality of intermediate reasoning steps, leaving models vulnerable to reward hacking. We argue that verbosity is not merely a length problem, but a symptom of poor intermediate reasoning quality. To investigate this, we conduct an empirical study tracking the per-token predictive entropy of large reasoning models across reasoning trajectories. We find that high-quality reasoning traces exhibit two consistent proper...
| Authors | Chengwei Wei, Jung-jae Kim, Longyin Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 March 2026 |
| Last updated at source | 4 June 2026 |
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