57 citations · 448 across the 99 of their papers we have counts for
13 papers · 1 filter
SPIRAL: Learning to Search and Aggregate
Jubayer Ibn Hamid, Ifdita Hasan Orney, Michael Y. Li +5
Language model reasoning can be substantially improved at test time via scaffolds that scale inference compute across different primitives -- sequential reasoning within a trace, i…
Poly-EPO: Training Exploratory Reasoning Models
Ifdita Hasan Orney, Jubayer Ibn Hamid, Shreya S Ramanujam +5
Exploration is a cornerstone of learning from experience: it enables agents to find solutions to complex problems, generalize to novel ones, and scale performance with test-time co…
Training Language Models for Social Deduction with Multi-Agent Reinforcement Learning
Bidipta Sarkar, Warren Xia, C. Karen Liu +1
Communicating in natural language is a powerful tool in multi-agent settings, as it enables independent agents to share information in partially observable settings and allows zero…
Diverse Conventions for Human-AI Collaboration
Bidipta Sarkar, Andy Shih, Dorsa Sadigh
Conventions are crucial for strong performance in cooperative multi-agent games, because they allow players to coordinate on a shared strategy without explicit communication. Unfor…
RoboCLIP: One Demonstration is Enough to Learn Robot Policies
Sumedh A Sontakke, Jesse Zhang, Sébastien M. R. Arnold +5
Reward specification is a notoriously difficult problem in reinforcement learning, requiring extensive expert supervision to design robust reward functions. Imitation learning (IL)…
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Stephen Casper, Xander Davies, Claudia Shi +29
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of…