ConMax: Confidence-Maximizing Compression for Efficient Chain-of-Thought Reasoning

• URL: http://arxiv.org/abs/2601.04973v1
• Date: Thu, 08 Jan 2026 14:22:58 GMT
• Title: ConMax: Confidence-Maximizing Compression for Efficient Chain-of-Thought Reasoning
• Authors: Minda Hu, Zexuan Qiu, Zenan Xu, Kun Li, Bo Zhou, Irwin King,
• Abstract summary: Large Reasoning Models generate redundant reasoning paths that inflate computational costs without improving accuracy.<n>In this paper, we introduce ConMax, a novel reinforcement learning framework designed to automatically compress reasoning traces.<n>Experiments across five reasoning datasets demonstrate that ConMax achieves a superior efficiency-performance trade-off.
• Score: 46.481679150652205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent breakthroughs in Large Reasoning Models (LRMs) have demonstrated that extensive Chain-of-Thought (CoT) generation is critical for enabling intricate cognitive behaviors, such as self-verification and backtracking, to solve complex tasks. However, this capability often leads to ``overthinking'', where models generate redundant reasoning paths that inflate computational costs without improving accuracy. While Supervised Fine-Tuning (SFT) on reasoning traces is a standard paradigm for the 'cold start' phase, applying existing compression techniques to these traces often compromises logical coherence or incurs prohibitive sampling costs. In this paper, we introduce ConMax (Confidence-Maximizing Compression), a novel reinforcement learning framework designed to automatically compress reasoning traces while preserving essential reasoning patterns. ConMax formulates compression as a reward-driven optimization problem, training a policy to prune redundancy by maximizing a weighted combination of answer confidence for predictive fidelity and thinking confidence for reasoning validity through a frozen auxiliary LRM. Extensive experiments across five reasoning datasets demonstrate that ConMax achieves a superior efficiency-performance trade-off. Specifically, it reduces inference length by 43% over strong baselines at the cost of a mere 0.7% dip in accuracy, proving its effectiveness in generating high-quality, efficient training data for LRMs.

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