TRiMS: Real-Time Tracking of Minimal Sufficient Length for Efficient Reasoning via RL

• URL: http://arxiv.org/abs/2603.17449v1
• Date: Wed, 18 Mar 2026 07:45:39 GMT
• Title: TRiMS: Real-Time Tracking of Minimal Sufficient Length for Efficient Reasoning via RL
• Abstract summary: We introduce a theoretical metric, MSL-Minimal Sufficient Length, to maximize Intelligence per Token.<n> TRiMS achieves over 80% CoT token reduction with a minor accuracy boost across all benchmarks.
• Score: 8.709290296692197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models achieve breakthroughs in complex reasoning via long chain-of-thought sequences. However, this often leads to severe reasoning inflation, causing substantial computational redundancy. To maximize Intelligence per Token, we introduce a theoretical metric, MSL-Minimal Sufficient Length. MSL rigorously characterizes the shortest reasoning length that preserves answer correctness. We provide a recursive definition based on independently sampled sequences and prove the existence of its limit, establishing the first measurable lower bound for reasoning-chain compression. Building on an analysis of mainstream CoT compression strategies, we identify key structural factors enabling a model to approach MSL. Based on these insights, we propose TRiMS which employs the GRPO algorithm in conjunction with MSL-based estimation during training, while mitigating instabilities during the training process through dynamic batch aggregation and advantage computation using batch-level standard deviation. TRiMS achieves over 80% CoT token reduction with a minor accuracy boost across all benchmarks.

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