Productive State-Space Exploration for Multi-turn RL in LLM Agents

Key points

• We argue that addressing these challenges requires more productive task-aligned exploration to improve the agent's understanding of environment dynamics.1
• Motivated by intrinsic curiosity-driven exploration, we propose a conditional state-space exploration framework that balances task completion with intrinsic information-seeking behavior.1
• Specifically, we introduce an intrinsic reward that penalizes actions whose observation duplicates prior state memory.1
• Strong experimental results demonstrate that our approach substantially improves policy learning in multi-turn environments, mitigating invalid actions and repetitive loops.1

Sources (1)

• [1]Productive State-Space Exploration for Multi-turn RL in LLM Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 07:49 AM

  Reinforcement Learning (RL) for Large Language Model (LLM) agents in multi-turn, long-horizon environments remains challenging due to inefficient action selection and repetitive behavioral patterns.

  We argue that addressing these challenges requires more productive task-aligned exploration to improve the agent's understanding of environment dynamics.

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