arXiv Artificial Intelligence

RSPO: Reward-Swap Policy Optimization for Multi-Turn LLM Agents

2 hours ago 11.08.2026 07:00 👁 2 views
RSPO: Reward-Swap Policy Optimization for Multi-Turn LLM Agents

Quick summary

arXiv:2607.04713v2 Announce Type: replace-cross Abstract: Reinforcement learning holds significant potential for training large language models (LLMs) to handle multi-turn interactive tasks. However, in long-horizon, multi-turn tasks characterized by sparse outcome rewards, directly training with outcome rewards often results in slow convergence due to the sparsity of signals and the lack of fine-grained feedback. Furthermore, the model may fail to learn successful trajectories that are not sampled during training, thereby limiting its performance. Conversely, while employing customized dense

Key takeaways

  • arXiv:2607.04713v2 Announce Type: replace-cross Abstract: Reinforcement learning holds significant potential for training large language models (LLMs) to handle multi-turn interactive tasks.
  • However, in long-horizon, multi-turn tasks characterized by sparse outcome rewards, directly training with outcome rewards often results in slow convergence due to the sparsity of signals and the lack of fine-grained feedback.
  • Furthermore, the model may fail to learn successful trajectories that are not sampled during training, thereby limiting its performance.

Why it matters

The significance is not only the legal text but how it changes product design. Decisions around “RSPO: Reward-Swap Policy Optimization for Multi-Turn LLM Agents” may reshape data collection, model training, output accountability and market access.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗