Papers

Filtered to on-policy learning · clear filter

Browse by term

continual learning 91reinforcement learning 43vision-language models 18benchmarking 12benchmarks 11robotics 11large language models 10language models 9world models 9video generation 6generative models 5multimodal models 5natural language processing 5recursive self-improvement 5video world models 5agents 4dialogue systems 4diffusion models 4foundation models 4multimodal large language models 4on-policy distillation 4reasoning 4self-distillation 4self-supervised learning 4conversational AI 3evaluation benchmarks 3human-computer interaction 3LLMs 3multi-agent systems 3policy optimization 3

Matching papers

Self-Supervised Visual On-Policy Distillation

158 upvotes · 14 AUG 2026 · Yijiang Li, Yijun Liang, Yunjie Tian et al.

This paper proposes a new method called Self-Supervised Visual On-Policy Distillation (S^2VOPD) that generates learning signals from asymmetric augmented views of images, allowing for effective on-policy learning without privileged information. Practitioners might care about this paper because it presents a simple yet effective way to improve performance on various perception benchmarks.

visual augmentationon-policy learningself-supervised learningdistillationreinforcement learning

Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation

138 upvotes · 28 SEP 2026 · Xin Li, Hao Jiang, Xin Gao et al.

This paper proposes a new approach to multi-teacher on-policy distillation that allows language models to learn multiple skills by balancing feedback from different specialists. A practitioner might care about this paper because it could lead to more versatile and effective language models that can handle a wide range of tasks.

reinforcement learningmulti-teacher distillationon-policy learningdomain adaptationfeedback balancing