RESEARCHMonitorWATCHLIST
VETTA: Coordinating Turn- and Token-Level Credit Assignment for Multi-Turn LLM Agents
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Researchers propose VETTA, a method for coordinating turn- and token-level credit assignment in multi-turn LLM agents.
Inspect the evidence
- Inclusion basis
- AI in finance
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 7 October 2026
- Collected by OneBench
- 8 Oct 2026, 03:02 UK
Stored source excerpt
arXiv:2610.08402v1 Announce Type: new Abstract: Multi-turn LLM agents often receive sparse task feedback across several interactions, while generating each response token by token. This creates…
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