Semantic Consistency Policy Optimization for Reinforcement Learning of LLM Agents

Researchers propose Semantic Consistency Policy Optimization (SCPO), a training method that improves how large language model agents learn from reinforcement learning by addressing a fundamental inconsistency: semantically similar intermediate steps receive contradictory credit signals based on whether their trajectory ultimately succeeds or fails. The approach recovers step-level credit from successful rollouts, achieving state-of-the-art performance on complex reasoning tasks like ALFWorld and WebShop.

SCPO addresses a critical inefficiency in how reinforcement learning trains language model agents on long-horizon tasks. Current group-based RL methods assign credit to individual steps based entirely on whether the final trajectory succeeds, creating a problematic situation where identical or near-identical actions receive opposite gradient signals. This semantic inconsistency wastes valuable learning signals embedded in partially-correct failed attempts and confuses the model about which actions are truly beneficial.

The breakthrough lies in SCPO's elegant solution: rather than discarding failed trajectories entirely, the method cross-references failed steps against successful siblings within the same rollout group. By identifying progress made in failed attempts relative to successful ones, SCPO assigns positive credit for novel contributions, even when the overall trajectory failed. This preserves learning signal from the progress achieved before failure occurred.

The empirical results validate this approach's effectiveness. Achieving 93.7% success on ALFWorld and 74.8% on WebShop at 1.5B parameters matches or exceeds existing baselines while being more sample-efficient. Notably, gains concentrate on the hardest multi-step reasoning tasks—exactly where semantically-consistent credit signals matter most. This advancement matters for deploying autonomous agents in real-world applications where long-horizon reasoning is essential.

The work highlights how seemingly minor algorithmic refinements in credit assignment can substantially improve LLM agent performance. As language models become increasingly capable at planning and tool use, better training methods become competitive advantages. Future research may extend this approach to other domains requiring complex sequential decision-making.

• →SCPO solves semantic credit inconsistency by comparing failed steps against successful siblings in rollout groups rather than assigning uniform failed-trajectory penalties.
• →The method achieves 93.7% success on ALFWorld and 74.8% on WebShop, matching or exceeding strong baselines while improving sample efficiency.
• →Performance gains concentrate on multi-step reasoning tasks where semantically-consistent credit signals provide the most learning value.
• →This advancement improves how reinforcement learning trains autonomous LLM agents by recovering learning signal from partially-correct failed attempts.
• →The value-free reward-shaping approach requires no additional environment feedback beyond standard trajectory outcomes.