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What Does Multi-Turn Reinforcement Learning Really Improve in LLM Agents?
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Research shows multi-turn RL performance gains in LLM agents depend heavily on turn budgets and do not consistently improve single-step decisions.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 5 November 2026
- Collected by OneBench
- 6 Nov 2026, 02:01 UK
Stored source excerpt
arXiv:2611.06483v1 Announce Type: cross Abstract: While multi-turn agentic reinforcement learning (RL) excels on long-horizon tasks, its performance gains remain highly sensitive to interaction constraints. Evaluating…
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