#d4rl-benchmarks News & Analysis

2 articles tagged with #d4rl-benchmarks. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · May 297/10

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Offline Reinforcement Learning with Generative Trajectory Policies

Researchers propose Generative Trajectory Policies (GTPs), a unified framework for offline reinforcement learning that bridges the performance gap between slow diffusion models and fast consistency policies by learning continuous-time generative trajectories. The approach achieves state-of-the-art results on D4RL benchmarks, including perfect scores on difficult AntMaze tasks.

AINeutralarXiv – CS AI · May 286/10

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SPAR: Support-Preserving Action Rectification

Researchers introduce SPAR (Support-Preserving Action Rectification), a new offline reinforcement learning method that addresses the fundamental tension between maximizing value and staying true to training data. By anchoring policy improvements to frozen behavior cloning and operating in residual space, SPAR achieves state-of-the-art results on D4RL benchmarks while maintaining data distribution fidelity.