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Matching papers

Eliciting Weak-to-Strong Generalization with On-Policy Reverse Distillation

78 upvotes · 8 SEP 2026 · Youngrok Park, Sangmin Bae, Hojung Jung et al.

This paper introduces a method called On-Policy Reverse Distillation that helps stronger models learn from weaker supervisors by selectively amplifying the parts of the weaker model's guidance that are most useful to the stronger model. This can lead to faster and more efficient learning, especially in situations where it's expensive to retrain models from scratch.

reinforcement learningpolicy optimizationdistillationtransfer learningmodel selection

RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments

70 upvotes · 14 SEP 2026 · Sibo Zhu, Shicheng Fan, Xinyue Wang et al.

This paper introduces a new framework called RSIAgent that helps digital agents adapt to new environments without needing to be retrained. A practitioner might care about this because it allows for more efficient and effective AI systems that can learn and improve on their own.

reinforcement learningmulti-agent systemsautonomous explorationrecursive self-improvementtransfer learning