BiPACE: Bisimulation-Guided Policy Optimization with Action Counterfactual Estimation for LLM Agents
arXiv:2606.25556v1 Announce Type: cross Abstract: Stepwise group-based RL is an attractive way to train long-horizon LLM agents without a learned critic: it reuses multiple sampled rollouts to estimate local advantages. Its weakness is less visible but more fundamental: every group-relative estimator assumes that the steps it compares are equivalent for credit assignment. We show that current agentic variants violate this assumption through a state-action credit mismatch. The observation-hash partition is overly fine on the state side, creating singleton groups with zero step-level signal, whi
This paper addresses a fundamental limitation in current stepwise group-based reinforcement learning for LLM agents, suggesting a more robust optimization method is needed as LLM agentic approaches mature.
Improving the training and reliability of LLM agents is critical for their adoption across various industries, enabling more complex and autonomous task execution.
The proposed BiPACE method offers a path towards more effective and stable policy optimization for LLM agents, potentially accelerating their development beyond current limitations.
- · AI agent developers
- · Companies implementing LLM agents
- · Reinforcement learning researchers
- · Developers relying on less efficient RL methods
- · Platforms with brittle agentic systems
More capable and robust LLM agents become available for deployment in enterprise and consumer applications.
Increased adoption of LLM agents begins to automate and condense white-collar workflows, impacting service sectors.
The enhanced autonomy of LLM agents could lead to new forms of 'agentic' economies and more complex human-like interactions with AI systems.
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Read at arXiv cs.LG