Conditional State-Space Exploration Framework
An exploration framework for multi-turn reinforcement learning of LLM agents that penalizes repeated observations through intrinsic rewards to encourage state-space coverage, balancing task completion against information seeking.
Timeline (last 90 days)
The authors propose a conditional state-space exploration framework that balances task completion and intrinsic information-seeking behavior.
Rather than adopting generic information-theoretic measures, the method in this paper constructs the reward using the signal of whether new observations repeat historical state memory.
Strong experimental results show that the method significantly improves policy learning in multi-turn environments, both alleviating the ineffective-action problem and suppressing repetitive loops.
The generality of this method still needs to be validated in more types of environments.
All Facts (4)
The authors propose a conditional state-space exploration framework that balances task completion and intrinsic information-seeking behavior.
50%UnverifiedRather than adopting generic information-theoretic measures, the method in this paper constructs the reward using the signal of whether new observations repeat historical state memory.
50%UnverifiedStrong experimental results show that the method significantly improves policy learning in multi-turn environments, both alleviating the ineffective-action problem and suppressing repetitive loops.
50%UnverifiedThe generality of this method still needs to be validated in more types of environments.
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