Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners

The paper introduces a method using high-capacity value models conditioned on learnable task embeddings to enable robust, scalable multi-task reinforcement learning, overcoming challenges like task interference and gradient conflicts. Tested on over 280 tasks across various benchmarks, this approach achieves state-of-the-art performance and efficient transfer, marking a significant advancement in multi-task RL.