Frames online zero-shot transfer with BFMs as a bandit problem and derives an eigenvalue-minimization exploration strategy under linear reward approximation.
Advances in neural information processing systems , volume=
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
LEO enables efficient all-goals learning in goal-conditioned RL by jointly predicting for all goals in one network pass, yielding >250x speedup over relabelling and better performance on Craftax.
An actor-critic RL algorithm for low-rank MDPs achieves improved sample efficiency using solely a policy evaluation oracle.
FootsiesGym is an open-source, vectorized fighting-game benchmark for two-player zero-sum imperfect-information RL that isolates non-transitive neutral-game dynamics while remaining tractable on standard hardware.
citing papers explorer
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Exploration and Online Transfer with Behavioral Foundation Models
Frames online zero-shot transfer with BFMs as a bandit problem and derives an eigenvalue-minimization exploration strategy under linear reward approximation.
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Goal-Conditioned Agents that Learn Everything All at Once
LEO enables efficient all-goals learning in goal-conditioned RL by jointly predicting for all goals in one network pass, yielding >250x speedup over relabelling and better performance on Craftax.
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Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs
An actor-critic RL algorithm for low-rank MDPs achieves improved sample efficiency using solely a policy evaluation oracle.
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FootsiesGym: A Fighting Game Benchmark for Two-Player Zero-Sum Imperfect-Information Games
FootsiesGym is an open-source, vectorized fighting-game benchmark for two-player zero-sum imperfect-information RL that isolates non-transitive neutral-game dynamics while remaining tractable on standard hardware.