Reasoning over long document contexts remains a challenge for large language models (LLMs). Standard retrieval methods are inherently reactive, bottlenecking agents with complex, cross-document reasoning at inference time, while precomputed structures like knowledge graphs often fall short because they rely on rigid he...
Anikait Singh, Teresa Zhang, Yoonho Lee et al.· 0 citations
This work introduces SeeQ (Subtask-elicited Q-functions), which instead learns Q-values for the currently active subtask, which substantially improves best-of-N policy steering on real-world manipulation tasks.
Saksham Singh, Zhe-Yuan Hu, Max Sobol Mark et al.· 0 citations
It is shown that a simple and unbiased dense reward formulation, which is term progressive point matching, scales exponentially more efficiently to long-horizon tasks by rewarding partial progress on a segment level, both theoretically and empirically via synthetic environments.
Preston Fu, Kevin Frans, Oleh Rybkin et al.· 0 citations
A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction. Yet fully autonomous learning remains difficult with current policies: sparse rewards and weak zero-shot exploration make it unlikely that a robot will discover successful be...
Shreyas Kowshik, Sreyas Venkataraman, L. Wang et al.· 1 citation
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