This paper investigates a common problem in reinforcement learning for language models called Value Flattening, where critics fail to accurately estimate state values, and proposes a new method, SP^3O, to mitigate this issue by supervising only a few well-separated states per response.
Firehose
Filtered to tagged “critic learning” · clear filters
Browse: People · Companies · Papers · Podcasts · Hacker News · Deep dives
Browse by tag
artificial intelligence 82continual learning 29AI 26agentic coding 17reinforcement learning 16open-weight models 11AI agents 10AI safety 9AI ethics 8cybersecurity 8machine learning 7open-source 7language models 6natural language processing 6Reinforcement learning 6artificial general intelligence 5deep learning 5Diffusion models 5Agentic AI 4computer vision 4conversational AI 4ethics 4existential risk 4large language models 4LLMs 4Recursive self-improvement 4robotics 4security 4vision-language models 4ai 3