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Think When You Need: Self-Adaptive Chain-of-Thought Learning
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Chain of Thought (CoT) reasoning enhances language models' performance but often leads to inefficient "overthinking" on simple problems. We identify that existing approaches directly penalizing reasoning length fail to account for varying problem complexity. Our approach constructs rewards through length and quality comparisons, guided by theoretical assumptions that jointly enhance solution correctness with conciseness. Moreover, we further demonstrate our method to fuzzy tasks where ground truth is unavailable. Experiments across multiple reasoning benchmarks demonstrate that our method maintains accuracy while generating significantly more concise explanations, effectively teaching models to "think when needed."
Forward citations
Cited by 10 Pith papers
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Probing the Difficulty Perception Mechanism of Large Language Models
LLMs linearly encode math-problem difficulty in their final-token representations, and specific final-layer attention heads are specialized for easy vs hard problems.
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Reconsidering Overthinking: Penalizing Internal and External Redundancy in CoT Reasoning
A dual-penalty RL method that compresses chain-of-thought traces by separately penalizing internal semantic stagnation and external post-answer continuation reduces reasoning length by about 40% while preserving accur...
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Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model
Suppressing 'thinking tokens' in a 1.5B reasoning model preserves accuracy while cutting tokens, and the proposed DuP-PO RL method improves both accuracy and efficiency over GRPO.
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How Far Are We from Optimal Reasoning Efficiency?
The authors define a reasoning efficiency frontier and a gap metric (REG), then train models with REO-RL to shrink the gap by at least 50% with only small accuracy losses.
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Efficient Long CoT Reasoning in Small Language Models
Binary cutting with on-policy validation prunes redundant chain-of-thought steps in teacher traces, letting 7B models keep most long-CoT accuracy while generating fewer tokens.
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VeriThinker: Learning to Verify Makes Reasoning Model Efficient
VeriThinker shows that fine-tuning a reasoning model only on a solution-verification task reduces chain-of-thought length on MATH500 and AIME by 20-45% while preserving or slightly improving accuracy.
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Failure Cases Are Better Learned But Boundary Says Sorry: Facilitating Smooth Perception Change for Accuracy-Robustness Trade-Off in Adversarial Training
A paper whose abstract describes new adversarial training experiments, but whose full text is a different paper on CoT compression, leaving the claims unsupported.
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Enhancing Large Language Models through Structured Reasoning
Structured reasoning tags plus a max-flow reward let a 1.5B model match the math accuracy of models trained for far longer, but the gains are within statistical noise.
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Don't Think Longer, Think Wisely: Optimizing Thinking Dynamics for Large Reasoning Models
DTO constructs compressed, ground-truth-curated reasoning trajectories from LRM outputs and uses them as preferred responses in SimPO, yielding up to 12% accuracy gains and roughly 40% token reduction on math benchmarks.
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Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey
A comprehensive review that categorizes methods for shortening and adaptively triggering chain-of-thought reasoning in large language models.
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