{"slug": "progress-conditioned-group-policy-optimization-for-long-horizon-agentic-tasks", "title": "Progress-conditioned Group Policy Optimization for Long-Horizon Agentic Tasks", "summary": "Researchers propose Progress-conditioned Group Policy Optimization (ProGPO), a method that uses first-visit observation coverage to assign higher advantages to trajectories visiting new states when all group samples receive zero outcome reward, breaking the credit trap in long-horizon agentic tasks. Experiments on ALFWorld and WebShop with Qwen2.5-1.5/7B-Instruct show ProGPO consistently improves over group-based baselines, with large gains on hard tasks.", "body_md": "arXiv:2607.22724v1 Announce Type: new\nAbstract: Group-based policy optimization has been increasingly used to train large language model (LLM) agents from sparse outcome rewards by comparing trajectories or steps within a group. However, on difficult long-horizon tasks, this comparison can suffer from a sampling imbalance: repeated or low-effect actions dominate the high-probability region of the policy while useful state-changing actions remain under-sampled. This imbalance produces many all-failed rollout groups, where outcome rewards provide no direction for correcting the policy. Together, these effects can form a self-reinforcing credit trap: failure-dominated sampling yields no outcome-based correction, allowing repeated low-effect actions to persist. To break this loop, we propose Progress-conditioned Group Policy Optimization (ProGPO), which uses first-visit observation coverage only when all samples in a group receive zero outcome reward. Specifically, within such groups, ProGPO assigns higher relative advantages to trajectories or steps that visit more new states since reaching new observations is a prerequisite for task success. Experiments on two challenging agentic benchmarks, ALFWorld and WebShop with Qwen2.5-1.5/7B-Instruct, show that ProGPO consistently improves over group-based baselines, with particularly large gains on hard tasks.", "url": "https://wpnews.pro/news/progress-conditioned-group-policy-optimization-for-long-horizon-agentic-tasks", "canonical_source": "https://arxiv.org/abs/2607.22724", "published_at": "2026-07-28 04:00:00+00:00", "updated_at": "2026-07-28 04:12:15.055420+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "machine-learning"], "entities": ["arXiv", "Qwen2.5-1.5", "Qwen2.5-7B-Instruct", "ALFWorld", "WebShop"], "alternates": {"html": "https://wpnews.pro/news/progress-conditioned-group-policy-optimization-for-long-horizon-agentic-tasks", "markdown": "https://wpnews.pro/news/progress-conditioned-group-policy-optimization-for-long-horizon-agentic-tasks.md", "text": "https://wpnews.pro/news/progress-conditioned-group-policy-optimization-for-long-horizon-agentic-tasks.txt", "jsonld": "https://wpnews.pro/news/progress-conditioned-group-policy-optimization-for-long-horizon-agentic-tasks.jsonld"}}