{"slug": "exrl-sample-efficient-online-adaptation-of-long-chunk-policies-via-execution", "title": "ExRL: Sample-Efficient Online Adaptation of Long-Chunk Policies via Execution Length Control", "summary": "A paper titled \"ExRL: Sample-Efficient Online Adaptation of Long-Chunk Policies via Execution Length Control\" reports that adapting long-chunk robot policies — which predict action chunks spanning dozens of timesteps — with existing reinforcement learning methods can require many online samples because of the high dimensionality involved. The paper proposes execution length control as a method for sample-efficient online adaptation of these policies. The work bears on how robot policies that predict long action horizons can be fine-tuned online with fewer samples.", "body_md": "Predicting long action chunks, often spanning dozens of timesteps, has become a key ingredient in increasingly capable robot policies. In this paper, we find that adapting these long-chunk policies with existing reinforcement learning (RL) methods can require many online samples due to the high dime", "url": "https://wpnews.pro/news/exrl-sample-efficient-online-adaptation-of-long-chunk-policies-via-execution", "canonical_source": "https://aiflash.com/news/149575/", "published_at": "2026-11-11 04:00:22+00:00", "updated_at": "2026-11-11 04:21:40.899149+00:00", "lang": "en", "topics": ["robotics", "machine-learning", "ai-research", "artificial-intelligence"], "entities": ["ExRL"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/exrl-sample-efficient-online-adaptation-of-long-chunk-policies-via-execution", "markdown": "https://wpnews.pro/news/exrl-sample-efficient-online-adaptation-of-long-chunk-policies-via-execution.md", "text": "https://wpnews.pro/news/exrl-sample-efficient-online-adaptation-of-long-chunk-policies-via-execution.txt", "jsonld": "https://wpnews.pro/news/exrl-sample-efficient-online-adaptation-of-long-chunk-policies-via-execution.jsonld"}}