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This paper introduces a framework called DeepSearch-Evolve, which helps train self-improving web agents by iteratively refining their performance using their own experience. Practitioners might care because this approach can lead to more efficient and effective agents that can learn from their own mistakes.
This paper introduces Visual Contrastive Self-Distillation, a method that removes the need for external teacher information and privileged answers in on-policy self-distillation, allowing for simpler and more efficient learning. Practitioners might care about this approach because it can lead to better performance in language models.
This paper develops a new method for improving reasoning language models, called β-OPSD, which combines policy optimization and self-distillation to improve stability and performance. Practitioners might care about this method because it provides a more efficient and effective way to improve language model reasoning abilities.
This paper improves a type of reinforcement learning (RL) called rubric-based RL, which helps large language models (LLMs) perform well on open-ended tasks. A practitioner might care about this paper because it addresses a common problem in RL, where some criteria (or rules) are not explored properly, and it shows that its new method can improve performance on these tasks.
This paper introduces H^2SD, a hybrid hindsight self-distillation framework for reinforcement learning with verifiable rewards, which combines the strengths of different methods to improve large language models' reasoning capabilities. Practitioners may care about this work because it addresses limitations of existing methods and shows promising results on challenging reasoning benchmarks.