3 papers
cs.LG2025
floq: Training Critics via Flow-Matching for Scaling Compute in Value-Based RL
Bhavya Agrawalla, Michal Nauman, Khush Agrawal +1
A hallmark of modern large-scale machine learning techniques is the use of training objectives that provide dense supervision to intermediate computations, such as teacher forcing…
cs.LG2025
Compute-Optimal Scaling for Value-Based Deep RL
Preston Fu, Oleh Rybkin, Zhiyuan Zhou +4
As models grow larger and training them becomes expensive, it becomes increasingly important to scale training recipes not just to larger models and more data, but to do so in a co…
cs.LG2025
Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners
Michal Nauman, Marek Cygan, Carmelo Sferrazza +2
Recent advances in language modeling and vision stem from training large models on diverse, multi-task data. This paradigm has had limited impact in value-based reinforcement learn…