Papers

Filtered to rubric-guided reinforcement learning · clear filter

Browse by term

continual learning 83reinforcement learning 50large language models 12benchmarking 11benchmarks 11language models 11vision-language models 11robotics 7natural language processing 6world models 6generative models 5recursive self-improvement 5attention mechanisms 4diffusion Transformers 4multi-agent systems 4multimodal learning 4multimodal models 4on-policy distillation 4self-distillation 4self-supervised learning 4transformers 4video generation 4vision-language-action models 4agent-based systems 3agentic models 3agentic search 3autonomous systems 3coding agents 3diffusion models 3image generation 3

Matching papers

CoRT: Counterfactual Replay for Token-Level Rubric-Guided Policy Optimization

73 upvotes · 28 JUL 2026 · Bo-Wen Zhang, Junwei He, Wen Wang et al.

This paper proposes a method to improve language model training by allocating credit to individual tokens within a response, allowing for more nuanced evaluation of model performance. Practitioners may care about this method as it can lead to better language model performance, especially in tasks that require specific formatting or semantic choices.

rubric-guided reinforcement learningcontinual learningtoken-level credit weightingcounterfactual replaypolicy optimization