RESEARCHMonitorWATCHLIST
AdaStep: Adaptive Step Credit Weighting for Agentic Reinforcement Learning
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Researchers propose AdaStep, an adaptive step-credit weighting method to improve reward allocation for long-horizon LLM agents.
Inspect the evidence
- Inclusion basis
- AI in finance
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 5 October 2026
- Collected by OneBench
- 6 Oct 2026, 03:01 UK
Stored source excerpt
arXiv:2610.03223v1 Announce Type: cross Abstract: Long-horizon LLM agents are typically trained with sparse outcome rewards, making trajectory-level objectives too coarse to distinguish the contribution of…
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