Source note2 min read
Semantic Consistency Policy Optimization for Reinforcement Learning of LLM Agents
Source
arXiv
arxiv.org/abs/2606.25852v1
Source
- Source name: arXiv
- Source kind: paper
- Authors / organization: Peng Xu, Sijia Chen, Junzhuo Li, Xuming Hu
- Venue: arXiv: cs.LG
- Published: 2026-06-24
- Collected: 2026-06-25
- URL: http://arxiv.org/abs/2606.25852v1
- PDF: https://arxiv.org/pdf/2606.25852v1
- DOI: UNKNOWN
Plain-Language Brief
Researchers propose Semantic Consistency Policy Optimization (SCPO), a method to optimize reinforcement learning for Language Model (LLM) agents. SCPO aims to reduce semantic credit inconsistencies that occur when similar actions receive opposite credit based on the success or failure of a trajectory. The method recovers step-level credit from successful siblings within the same rollout group, improving performance on ALFWorld and WebShop tasks.
What the Document Says
- SCPO is a value-free reward-shaping method for LLM agents.
- SCPO aims to mitigate semantic credit inconsistencies in group-based reinforcement learning.
- SCPO recovers step-level credit from successful siblings within the same rollout group.
- SCPO has been tested on ALFWorld and WebShop tasks.
- On ALFWorld, SCPO reaches 93.7+/-4.1 percent success at 1.5B parameters.
- On WebShop, SCPO reaches 74.8+/-2.0 percent success at 1.5B parameters.
Key Concepts Mentioned
- Semantic Consistency Policy Optimization (SCPO)
- Language Model (LLM) agents
- Group-based reinforcement learning
- ALFWorld
- WebShop
Claims Made by the Document
- SCPO mitigates semantic credit inconsistencies.
- SCPO improves performance on ALFWorld and WebShop tasks.
Evidence Used
- Recovery of step-level credit from successful siblings in the same rollout group.
- Results showing success rates on ALFWorld and WebShop tasks at 1.5B parameters.
Methods / Approach
UNKNOWN
Limitations Stated by the Authors
UNKNOWN
Limitations Noted by the Agent
Agent interpretation — not from the source.
UNKNOWN
Open Questions
UNKNOWN
Notes for Future Q&A
May include interpretation. Labeled where it does.
UNKNOWN
Raw Abstract / Excerpt
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 credit from successful siblings in the same rollout group. Concretely, SCPO scores each failed step against a successful sibling and adds positive step-level credit for new progress along that sibling. On ALFWorld and WebShop, SCPO matches or exceeds strong group-based baselines, reaching 93.7+/-4.1 percent success on ALFWorld and 74.8+/-2.0 percent on WebShop at 1.5B parameters, with gains concentrated on the hardest multi-step tasks.
