When Denser Credit Is Not Enough: Evidence-Calibrated Policy Optimization for Long-Horizon LLM Agent Training
arXiv:2606.05885v1 Announce Type: new Abstract: Long-horizon LLM agents require reinforcement learning methods that can assign credit to intermediate decisions under sparse and delayed rewards. Recent group-based methods such as GiGPO improve over GRPO by constructing step-level advantages at repeated anchor states. However, we show that such dense credit can be statistically unreliable: under limited rollouts, rare but lucky actions may receive overly large advantages, producing divergent anchor bias and late-stage training oscillation. We propose Evidence-Calibrated Policy Optimization (ECPO
This research addresses a critical challenge in training long-horizon LLM agents, which is topical due to rapid advancements and increased focus on agentic AI systems.
Improved training methods for LLM agents will accelerate their capabilities, making complex white-collar automation and other advanced AI applications more feasible and reliable.
The proposed 'Evidence-Calibrated Policy Optimization' (ECPO) method offers a more stable and reliable approach to reinforcement learning for sophisticated AI agents, potentially overcoming current limitations in dense credit assignment.
- · AI developers
- · Companies implementing AI agents
- · Research institutions
- · Companies relying on less efficient RL training methods
- · Workflows currently resistant to automation
More robust and general-purpose LLM agents become deployable across various industries.
Increased adoption of AI agents could lead to significant productivity gains and workflow displacement in knowledge work.
The enhanced autonomy of these agents might accelerate the development of more complex and adaptive AI systems, pushing boundaries of current AI capabilities.
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Read at arXiv cs.LG