Papers by Zhengxiang Cheng
Optimizing Length Compression in Large Reasoning Models (2026.acl-long)
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| Challenge: | Large Reasoning Models suffer from producing unnecessary and verbose reasoning chains. |
| Approach: | They propose a post-training method that uses a Length Reward and a Compress Reward to remove the invalid portion of the thinking process. |
| Outcome: | The proposed method reduces sequence length by 50% with only a marginal (2%) drop in accuracy. |