Absorbability-Guided CoT Compression for Efficient Reasoning

Reasoning models often generate longer chains of thought (CoTs) than a task requires, increasing inference cost. Accuracy-preserving compression requires the current model to absorb the compressed supervision into its reasoning capability. Existing methods choose compression levels from trajectory or task properties without evaluating this model-dependent condition. We introduce **Absorbability-Guided CoT Compression** (${\mathrm{AGC}}^2$), which uses local adaptation to select model-absorbable supervision. For each batch, ${\mathrm{AGC}}^2$ measures reference-trajectory quality and evaluates candidate token budgets through short SFT updates on a fresh copy of the current model. When reference trajectories are reliable, it selects the shortest budget whose adapted accuracy remains within a tolerance of the reference accuracy. When they are unreliable, it increases the budget until the adapted model reaches an absolute accuracy threshold. Across DeepSeek-R1-Distill-Qwen models at 7B, 14B, and 32B scales on GSM8K, MATH-500, and AIME2024, ${\mathrm{AGC}}^2$ reduces response length by 19.2%–67.3% while matching or exceeding original-model accuracy in seven of nine model–benchmark settings. These results establish model absorbability as the governing criterion for accuracy-preserving CoT compression: the retained budget matches the amount of supervision the current model absorbs into its reasoning capability.