SCA: Segment-Wise CoT Compression with Answer Alignment
Chain-of-thought (CoT) reasoning improves problem solving, but long think traces increase inference cost. Existing CoT compression methods usually optimize completion-level length. For structured thinking models, however, a completion contains both a think segment and an answer segment, so completion-level compression can save tokens by compressing not only the CoT but also the answer. We call this failure mode answer drift. We propose Segment-wise CoT Compression with Answer Alignment (SCA), an answer-preserving think-compression method. SCA parses completions into functional segments, routes compression rewards only to successful think tokens, and protects answer tokens through length a...
| Authors | Ye Tian, Hongyu Lin |
|---|---|
| Primary category | cs.AI |
| Published | 8 March 2026 |
| Last updated at source | 20 July 2026 |
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