{"slug": "guidance-contrastive-token-credit-assignment-for-discrete-policy-optimization", "title": "Guidance Contrastive Token Credit Assignment for Discrete Policy Optimization", "summary": "Researchers have developed Guidance Contrastive Policy Optimization (GCPO), a new algorithm that assigns per-token credit by contrasting model predictions under positive and negative prompts, addressing the limitation of uniform credit assignment in group-advantage-based reinforcement learning methods like GRPO and DAPO. GCPO outperformed these baselines on text-to-image generation and chain-of-thought reasoning benchmarks by emphasizing semantically relevant regions and critical keywords. The method offers a more precise and scalable optimization strategy for discrete policy learning across diverse domains.", "body_md": "arXiv:2605.29198v1 Announce Type: new\nAbstract: Group-advantage-based reinforcement learning methods, such as GRPO and DAPO, have demonstrated strong performance across diverse domains, including mathematical reasoning and text-to-image generation. However, their reliance on sample-level rewards introduces a key limitation as uniform credit assignment across all tokens fails to capture fine-grained, token-level contributions. To address this issue, we propose Guidance Contrastive Policy Optimization (GCPO), a novel algorithm that enables per-token credit assignment by contrasting model predictions under positive and negative prompts. Rather than uniformly broadcasting sample-level advantages, GCPO assigns token-level advantages proportional to the difference between these contrastive predictions, allowing more precise and informative learning signals. Empirically, we find that GCPO emphasizes semantically relevant regions such as visual areas aligned with textual prompts in text-to-image generation, and critical keywords within reasoning traces for chain-of-thought tasks. Through extensive experiments, GCPO consistently outperforms GRPO and DAPO baselines on both text-to-image generation and chain-of-thought reasoning benchmarks, demonstrating its effectiveness as a general and scalable optimization strategy for discrete policy learning.", "url": "https://wpnews.pro/news/guidance-contrastive-token-credit-assignment-for-discrete-policy-optimization", "canonical_source": "https://arxiv.org/abs/2605.29198", "published_at": "2026-05-29 04:00:00+00:00", "updated_at": "2026-05-29 04:16:27.185661+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "generative-ai", "natural-language-processing"], "entities": ["GRPO", "DAPO", "GCPO"], "alternates": {"html": "https://wpnews.pro/news/guidance-contrastive-token-credit-assignment-for-discrete-policy-optimization", "markdown": "https://wpnews.pro/news/guidance-contrastive-token-credit-assignment-for-discrete-policy-optimization.md", "text": "https://wpnews.pro/news/guidance-contrastive-token-credit-assignment-for-discrete-policy-optimization.txt", "jsonld": "https://wpnews.pro/news/guidance-contrastive-token-credit-assignment-for-discrete-policy-optimization.jsonld"}}