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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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
Universal horizon models extend geometric horizon models to arbitrary horizons and apply winsorized distributions for stable offline RL value learning, outperforming baselines on 100 OGBench tasks.
QueST replaces local point tracking with persistent semantic queries that globally attend to spatio-temporal features and apply 3D grounding to suppress drift, cutting absolute point error by 67.7% versus TAP-Net on long articulated sequences.
LASER trains a reinforcement learning policy inside a latent dynamics model to choose sensor placements that improve reconstruction of continuum fields under sparsity.
citing papers explorer
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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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Offline Reinforcement Learning with Universal Horizon Models
Universal horizon models extend geometric horizon models to arbitrary horizons and apply winsorized distributions for stable offline RL value learning, outperforming baselines on 100 OGBench tasks.
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QueST: Persistent Queries as Semantic Monitors for Drift Suppression in Long-Horizon Tracking
QueST replaces local point tracking with persistent semantic queries that globally attend to spatio-temporal features and apply 3D grounding to suppress drift, cutting absolute point error by 67.7% versus TAP-Net on long articulated sequences.
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LASER: Learning Active Sensing for Continuum Field Reconstruction
LASER trains a reinforcement learning policy inside a latent dynamics model to choose sensor placements that improve reconstruction of continuum fields under sparsity.