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Prompt-Driven Exploration: Language as an Exploration Space for VLA Reinforcement Learning
Sunshine Jiang, John Marangola, David Zhang +6
Exploration is essential to RL since a policy cannot improve by repeatedly sampling the behaviors it already prefers. Standard methods inject stochasticity in the action space, but…
RL-finetuning LLMs from on- and off-policy data with a single algorithm
Yunhao Tang, Taco Cohen, David W. Zhang +2
We introduce a novel reinforcement learning algorithm (AGRO, for Any-Generation Reward Optimization) for fine-tuning large-language models. AGRO leverages the concept of generation…
Soft Policy Optimization: Online Off-Policy RL for Sequence Models
Taco Cohen, David W. Zhang, Kunhao Zheng +3
RL-based post-training of language models is almost exclusively done using on-policy methods such as PPO. These methods cannot learn from arbitrary sequences such as those produced…
Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks
Shuai Wang, David W. Zhang, Jia-Hong Huang +4
Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks. The development of Hypergraph Neural…