Frames online zero-shot transfer with BFMs as a bandit problem and derives an eigenvalue-minimization exploration strategy under linear reward approximation.
arXiv preprint arXiv:2504.11054 , year=
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
ROVER pretrains transferable exploration policies by maximizing occupancy coverage with a learned resolvent world model and virtual sink state, outperforming baselines on sparse navigation tasks.
MotionPyramid learns a stack of latent decoders from motion tracking data to create multi-resolution action interfaces for RL policies in humanoid control, with residual interfaces allowing coarse programs and fine corrections to coexist.
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.
OMG is a diffusion model for omni-modal whole-body humanoid motion generation that uses language, audio, and reference motions after large-scale data curation to achieve state-of-the-art performance and adaptation.
Introduces a successor-measure adaptation that separates market dynamics from trading objectives inside the Avellaneda-Stoikov HJB framework to enable zero-shot quote adjustment.
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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Reward-free Pretraining for Reinforcement Learning via Occupancy Coverage Maximization
ROVER pretrains transferable exploration policies by maximizing occupancy coverage with a learned resolvent world model and virtual sink state, outperforming baselines on sparse navigation tasks.
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MotionPyramid: Hierarchical Motion Representation and Residual Interfaces
MotionPyramid learns a stack of latent decoders from motion tracking data to create multi-resolution action interfaces for RL policies in humanoid control, with residual interfaces allowing coarse programs and fine corrections to coexist.
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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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OMG: Omni-Modal Motion Generation for Generalist Humanoid Control
OMG is a diffusion model for omni-modal whole-body humanoid motion generation that uses language, audio, and reference motions after large-scale data curation to achieve state-of-the-art performance and adaptation.
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Zero-shot adaptation to order book dynamics
Introduces a successor-measure adaptation that separates market dynamics from trading objectives inside the Avellaneda-Stoikov HJB framework to enable zero-shot quote adjustment.