Papers by Lexiang Tang
LEASH: Adaptive Length Penalty and Reward Shaping for Efficient Large Reasoning Model (2026.acl-long)
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| Challenge: | Existing approaches to long reasoning traces are hard to tune and fail to adapt to evolving LLMs. |
| Approach: | They propose a reinforcement learning framework that optimizes the length of reasoning traces by a Lagrangian primal–dual method. |
| Outcome: | The proposed framework reduces the average reasoning length by 60% across diverse tasks while maintaining competitive performance. |