RESEARCHMonitorWATCHLIST
Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Researchers propose tree-aware posterior distillation to address sparse credit assignment and policy staleness in long-horizon agentic RL.
Related reporting
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Inspect the evidence
- Inclusion basis
- AI in finance
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 19 October 2026
- Collected by OneBench
- 20 Oct 2026, 03:01 UK
Stored source excerpt
arXiv:2610.23181v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards has become a scalable post-training paradigm for large language models. However, training agents over long-horizon…
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