arXiv:2606.25852v1 Announce Type: new Abstract: Group-based reinforcement learning effectively post-trains LLM agents for long-horizon, sparse-reward tasks by deriving step-level credit from trajectory outcomes. However, this ties a step's credit to its rollout's final outcome: semantically near-identical intermediate steps receive opposite credit depending on whether their trajectory eventually succeeded or failed. Such semantic credit inconsistency sends conflicting gradients to similar actions and wastes the partially-correct progress inside failed rollouts. Motivated by this, we propose Se
The rapid development and deployment of LLM agents for complex tasks highlights existing limitations in their ability to learn efficiently from long-horizon, sparse-reward environments, driving research into improved training methodologies.
This research addresses a core challenge in the effective and scalable training of AI agents, which is crucial for advancing autonomous systems capable of complex decision-making and human-like interaction.
The proposed 'Semantic Consistency Policy Optimization' aims to improve the learning efficiency and robustness of LLM agents by providing more consistent credit assignment, reducing wasted computational effort and accelerating development.
- · AI researchers
- · LLM developers
- · enterprises deploying AI agents
- · AI models without semantic consistency
- · inefficient reinforcement learning methods
More efficient and capable LLM agents become feasible for a wider array of complex, real-world tasks.
Reduced development costs and faster iteration cycles for agentic AI applications lead to quicker market adoption.
The enhanced reliability of LLM agents could accelerate the automation of white-collar workflows, impacting service industries significantly.
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Read at arXiv cs.LG