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The paper identifies the Matthew Effect in reinforcement learning for large language models, where easy problems improve more than hard ones, and introduces Never Give Up (NGU), an adaptive sampling method to allocate more compute to hard problems, demonstrating performance gains on math and coding benchmarks.
This paper introduces Lightning Weave, a framework that combines separately trained reasoning capabilities into one efficient model using on-policy distillation, enhancing accuracy and reducing token usage in math and code tasks.
This paper proposes Concretized Proposition Prompting (CPP), a framework that resolves the composition-knowledge dichotomy in LLMs by explicitly concretizing propositions relevant to questions, significantly enhancing reasoning performance especially in medical and math benchmarks.
This paper introduces DASH, a method that uses intermediate answer commitments within reasoning traces to assign segment-level credit, reducing overthinking behaviors and improving accuracy on competition-level math benchmarks.
This paper proposes Trajectory-Augmented Policy Optimization (TAPO), which constructs micro-reflective correction trajectories using the model's own correct and incorrect rollouts to improve reasoning in large language models, outperforming standard self-distillation methods on math benchmarks.
This paper formalizes reasoning redundancy in LLMs as the fraction of trailing steps that can be truncated without affecting correctness, quantifying 61-93% redundancy across frontier models and proving that redundancy is a structural consequence of length-agnostic outcome rewards.