EPiC: Towards Lossless Speedup for Reasoning Training through Edge-Preserving CoT Condensation

Jinghan Jia, Hadi Reisizadeh, Chongyu Fan, Nathalie Baracaldo, Mingyi Hong, Sijia Liu

Keywords: LLM Reasoning, CoT, Reasoning Model Training

Abstract: Large language models (LLMs) have shown remarkable reasoning capabilities when trained with chain-of-thought (CoT) supervision. However, the long and verbose CoT traces, especially those distilled from large reasoning models (LRMs) such as DeepSeek-R1, significantly increase training costs during the distillation process, where a non-reasoning base model is taught to replicate the reasoning behavior of an LRM. In this work, we study the problem of CoT condensation for resource-efficient reasoning training, aimed at pruning intermediate reasoning steps (i.e., thoughts) in CoT traces, enabling supervised model training on length-reduced CoT data while preserving both answer accuracy and the model’s ability to generate coherent reasoning. Our rationale is that CoT traces typically follow a three-stage structure: problem understanding, exploration, and solution convergence. Through empirical analysis, we find that retaining the structure of the reasoning trace, especially the early stage of problem understanding (rich in reflective cues) and the final stage of solution convergence (which closely relates to the final answer), is sufficient to achieve lossless reasoning supervision. To this end, we propose an Edge-Preserving Condensation method, EPiC, which selectively retains only the initial and final segments of each CoT trace while discarding the middle portion. This design draws an analogy to preserving the “edge” of a reasoning trajectory, capturing both the initial problem framing and the final answer synthesis, to maintain logical continuity. Our analyses leveraging the CoT landscape and measuring the mutual information between CoT steps provide further validation for this design. Experiments across multiple model families (Qwen and LLaMA) and benchmarks show that EPiC reduces training time by over 34% while achieving lossless reasoning accuracy (e.g., on MATH500), comparable to full CoT supervision. Additionally, we show that EPiC outperforms other condensation methods, including teacher-guided regeneration of condensed CoTs.

Primary Area: foundation or frontier models, including LLMs

Submission Number: 3173