{"slug": "proas-a-progress-aware-advantage-shaping-framework-for-multi-turn-agentic", "title": "ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning", "summary": "A paper titled \"ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning\" proposes progress-aware advantage shaping to address what its authors call the uniform credit assignment dilemma in critic-free reinforcement learning algorithms such as GRPO, which broadcast trajectory-level advantages equally to all actions when optimizing Large Language Model agents. The work targets multi-turn agentic reinforcement learning, where RL has become the primary paradigm for optimizing LLM agents.", "body_md": "Reinforcement learning (RL) has emerged as the primary paradigm for optimizing Large Language Model (LLM) agents, yet efficient critic-free algorithms (e.g., GRPO) suffer from a fundamental uniform credit assignment dilemma that broadcasts trajectory-level advantages equally to all actions. Our anal", "url": "https://wpnews.pro/news/proas-a-progress-aware-advantage-shaping-framework-for-multi-turn-agentic", "canonical_source": "https://aiflash.com/news/140234/", "published_at": "2026-10-27 03:30:11+00:00", "updated_at": "2026-10-27 03:43:10.870242+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-agents", "ai-research"], "entities": ["ProAS", "GRPO"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/proas-a-progress-aware-advantage-shaping-framework-for-multi-turn-agentic", "markdown": "https://wpnews.pro/news/proas-a-progress-aware-advantage-shaping-framework-for-multi-turn-agentic.md", "text": "https://wpnews.pro/news/proas-a-progress-aware-advantage-shaping-framework-for-multi-turn-agentic.txt", "jsonld": "https://wpnews.pro/news/proas-a-progress-aware-advantage-shaping-framework-for-multi-turn-agentic.jsonld"}}