Policy-Conditioned Counterfactual Credit for Verifiable Reinforcement Learning of Long-Horizon Language Agents
arXiv:2606.05263v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves reasoning and tool use, yet long-horizon language agents still learn unsupported evidence chains, belief drift, and shortcut actions that satisfy terminal checks. Existing process rewards are mostly correlational: they reward retrieval-, reflection-, or verification-like steps without estimating whether the step contributes to final verified success under a specified intervention. We propose CVT-RL, a constrained policy-gradient algorithm with dense verifiable rewards, intervention-validity
The increasing sophistication and widespread deployment of large language models in agentic contexts highlight the urgent need for verifiable and reliable AI behavior, particularly in complex, long-horizon tasks.
This research addresses a critical limitation of current AI agents, specifically their tendency for 'belief drift' and unreliable reasoning, which is essential for safely and effectively integrating them into high-stakes environments.
The introduction of policy-conditioned counterfactual credit and dense verifiable rewards provides a mechanism to train AI agents that are more transparent, robust, and less prone to generating incorrect or unsupported actions.
- · AI Safety Researchers
- · Enterprises deploying AI agents
- · Developers of foundational AI models
- · AI systems prone to hallucination
- · Unverified agentic AI applications
- · Developers relying solely on sparse rewards
AI agents will exhibit improved reliability and trustworthiness in executing long-horizon tasks, reducing the human oversight required.
Increased confidence in AI agent performance will accelerate their adoption across critical sectors, potentially collapsing more complex white-collar workflows.
The development of highly verifiable and auditable AI agents could lead to new regulatory frameworks and industry standards for AI autonomy and accountability.
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