DeepCompress: Smarter Reasoning, Fewer Tokens

DeepCompress: Smarter reasoning with fewer tokens

Large reasoning models often overthink easy questions and underthink hard ones. DeepCompress fixes this with a dual reward strategy that teaches models when to be brief and when to explore.

• Adaptive difficulty check: The system classifies each problem as Simple or Hard in real time based on the model’s evolving ability.
• Right-sized reasoning: It rewards shorter chains of thought for Simple tasks and encourages longer, exploratory reasoning for Hard ones.

Result: higher accuracy and fewer tokens—no trade-off required. On challenging math benchmarks, DeepCompress outperforms standard SFT and RL baselines while cutting unnecessary reasoning.

Paper: http://arxiv.org/abs/2510.27419v1

Paper: http://arxiv.org/abs/2510.27419v1

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#AI #MachineLearning #NLP #Reasoning #LargeLanguageModels #Efficiency #ReinforcementLearning #Research