Fewer Tokens, Smaller Cache: Reward-Coordinated Efficient Reasoning
Large Reasoning Models (LRMs) excel on complex tasks through long chain-of-thought (CoT) reasoning, but their lengthy intermediate steps cause severe overthinking that inflates inference cost. KV-cache compression is a common solution, yet existing reasoning-oriented methods apply a uniform policy across the trajectory and judge compression only by what it removes from the cache. Two observations point the other way. First, a reasoning state's tolerance to context loss varies along the trajectory, and process reward tracks it: deleting tokens at high-reward steps preserves accuracy far better than deleting the same budget at random. Second, compression is not free on the generation side, ...
| Authors | Qiyuan Zhu, Dezhi Li, Pengyu Cheng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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