Papers with Token-level Marginal Utility
Think Better, Not Longer: Token-Level Marginal Utility for Efficient Reasoning in Large Reasoning Models (2026.acl-long)
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| Challenge: | Large reasoning models (LRMs) generate explicit Chain-of-Thought rationales, but often suffer from "overthinking". |
| Approach: | They propose a unified training framework to synthesize concise reasoning chains by identifying tokens that reduce the model’s likelihood of the correct answer. |
| Outcome: | Experiments on deepSeek-R1-Distill-Qwen backbones show that MUTO yields better efficiency-accuracy Pareto frontier. |