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Can Language Models Learn to Skip Steps?
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Trained on vast corpora of human language, language models demonstrate emergent human-like reasoning abilities. Yet they are still far from true intelligence, which opens up intriguing opportunities to explore the parallels of humans and model behaviors. In this work, we study the ability to skip steps in reasoning - a hallmark of human expertise developed through practice. Unlike humans, who may skip steps to enhance efficiency or to reduce cognitive load, models do not inherently possess such motivations to minimize reasoning steps. To address this, we introduce a controlled framework that stimulates step-skipping behavior by iteratively refining models to generate shorter and accurate reasoning paths. Empirical results indicate that models can develop the step skipping ability under our guidance. Moreover, after fine-tuning on expanded datasets that include both complete and skipped reasoning sequences, the models can not only resolve tasks with increased efficiency without sacrificing accuracy, but also exhibit comparable and even enhanced generalization capabilities in out-of-domain scenarios. Our work presents the first exploration into human-like step-skipping ability and provides fresh perspectives on how such cognitive abilities can benefit AI models.
Forward citations
Cited by 10 Pith papers
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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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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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Assembly of Experts: Linear-time construction of the Chimera LLM variants with emergent and adaptable behaviors
Weight interpolation between two DeepSeek models, restricted to routed expert tensors, yields a 671B Chimera model with near-R1 reasoning and roughly 40% lower output-token count.
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Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning
Self-Route uses hidden-layer representations from a brief pre-inference plan to route each question to either short or long chain-of-thought, cutting tokens by 30-55% with under 2% accuracy loss.
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System-1.5 Reasoning: Traversal in Language and Latent Spaces with Dynamic Shortcuts
System-1.5 Reasoning lets LLMs reason in latent space with early exits and step-skipping, matching chain-of-thought accuracy at over 20x speedup on GSM8K and StrategyQA.
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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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Fast on the Easy, Deep on the Hard: Efficient Reasoning via Powered Length Penalty
Reward shaping with a powered length penalty makes LLMs answer easy questions with far fewer tokens while preserving or slightly improving accuracy on hard math benchmarks.
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Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models
A fine-tuned selector can pick long or short chain-of-thought per question and per token budget, cutting reasoning-model inference cost by roughly 50 percent without sacrificing accuracy.
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Fast Quiet-STaR: Thinking Without Thought Tokens
Fast Quiet-STaR uses a curriculum to compress Quiet-STaR's token-level thoughts and an RL stage to remove them entirely, improving accuracy on four benchmarks at the same or lower inference cost.
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PREMISE: Scalable and Strategic Prompt Optimization for Efficient Mathematical Reasoning in Large Models
The paper reports that a concise optimized prompt reduces reasoning tokens by up to 87.5% while roughly preserving accuracy on GSM8K, SVAMP, and MATH-500.
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