CORE: Contrastive Reflection Enables Rapid Improvements in Reasoning
Summary
Contrastive Reflection (CORE) is a non-parametric algorithm that generates concise, interpretable insights from comparing successful and unsuccessful reasoning traces, enabling faster and more efficient self-improvement for language models with fewer samples and rollouts than existing methods.
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Paper page - CORE: Contrastive Reflection Enables Rapid Improvements in Reasoning
Source: https://huggingface.co/papers/2605.28742
Abstract
Contrastive Reflection (CORE) improves language model reasoning by analyzing differences between successful and unsuccessful attempts to generate concise, interpretable insights that enable faster and more efficient self-improvement compared to traditional parametric and non-parametric approaches.
Language models can useverifiable rewardsto improve at a wide variety ofreasoning tasks. However, both parametric (e.g. RLVR) and non-parametric (e.g. prompt optimization) approaches to doing so typically require hundreds oftraining samplesand thousands ofmodel rollouts, making them expensive in the best case and intractable in the worst. To address this challenge, we introduceContrastive Reflection(CORE), a non-parametric learning algorithm that compares pastreasoning tracesto generate insights: shortnatural-language descriptionsofreasoning strategiesandconstraintsthat capture differences between successful and unsuccessful problem attempts. Across fourreasoning tasks, we demonstrate that CORE enables more rapid improvement than both parametric (GRPO) and non-parametric (GEPA,episodic RAG, andMemRL) methods, while using fewer rollouts. Under fixedrollout budgetswith as few as fivetraining samples, we then show that CORE also achieves comparable or greater performance gains than each baseline. Finally, we highlight how CORE is also substantially more context-efficient than non-parametric baselines, requiring fewerprompt tokenswhile storing learned knowledge as compact, interpretable natural-language insights. Our results therefore suggest that distilling contrasts between successful and unsuccessfulreasoning tracesinto abstract and useful insights can provide a more efficient and interpretable route to modelself-improvementthan weight updates, prompt optimization, or direct reuse of storedreasoning traces.
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