arXiv:2608. 01205v1 Announce Type: new Abstract: Recent offline reinforcement learning methods increasingly rely on expressive generative policies and specialized value-guidance mechanisms.
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arXiv:2609.36812v1 Announce Type: cross Abstract: Diffusion and flow policies can model complex behaviors in offline reinforcement learning (RL). However, penalizing their KL divergence from the beha...
Diffusion and flow policies can model complex behaviors in offline reinforcement learning (RL). However, penalizing their KL divergence from the behavior policy can discourage actions having high crit...
The paper introduces LAC (Light Actor, deep Critic), an offline reinforcement learning approach that allocates model capacity to a deep critic rather than a complex actor to improve inference efficiency. It addresses three failure modes—optimization, bootstrap-noise amplification, and value-range drift—using a residual MLP backbone, n‑step bootstrap targets, and a categorical cross‑entropy loss. Experiments on OGBench show LAC matches state‑of‑the‑art diffusion and flow‑matching baselines while reducing inference latency by up to four times.
arXiv:2608. 16798v1 Announce Type: cross Abstract: Agent harnesses have substantially improved performance on long-horizon tasks by coordinating agent interactions with the environment.
arXiv:2608. 07719v1 Announce Type: new Abstract: Offline reinforcement learning repeatedly trains policies from a fixed transition pool, making redundant data costly across seeds and hyperparameters, while naive subsampling can remove rare transitions needed for long-horizon credit assignment.
arXiv:2608.20909v1 Announce Type: new Abstract: Offline RL methods commonly jointly train the actor and critic, where the critic is used to guide the actor toward higher-value actions. This coupled l...