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News/Multi-Turn Reinforcement Learning for Tool-Calling Agents with Iterative Reward Calibration
arxiv
PublishedApril 6, 2026 at 4:00 AM
▲bullish

Multi-Turn Reinforcement Learning for Tool-Calling Agents with Iterative Reward Calibration

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2604.02869v1 Announce Type: new Abstract: Training tool-calling agents with reinforcement learning on multi-turn tasks remains challenging due to sparse outcome rewards and difficult credit assignment across conversation turns. We present the first application of MT-GRPO (Multi-Turn Group Rela

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Discussion
Mentioned models
05
  • 01
    Qwen3.5-4B
  • 02
    Qwen3-30B-A3B
  • 03
    GPT-4.1
  • 04
    GPT-4o
  • 05
    Claude Sonnet 4.5
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#reinforcement-learning#conversational-ai#benchmark#optimization

No replies yet. Be first.

Mentioned models
05
  • 01
    Qwen3.5-4B
  • 02
    Qwen3-30B-A3B
  • 03
    GPT-4.1
  • 04
    GPT-4o
  • 05
    Claude Sonnet 4.5
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#reinforcement-learning#conversational-ai#benchmark#optimization

Related coverage

More from ARXIV
arxivWhen Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems17harxivQuery-Driven Learning for Lifted Reasoning17h
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