Semantic Consistency Policy Optimization for Reinforcement Learning of LLM Agents
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 Semantic Consistency Policy Optimization (SCPO), a value-free reward-shaping method that mitigates this inconsistency by recovering step-level cred...
| Authors | Peng Xu, Sijia Chen, Junzhuo Li et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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