{"slug": "ppo-hsc-an-exploratory-reinforcement-learning-framework-based-on-wide-area", "title": "PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization", "summary": "Researchers introduced PPO-HSC (Proximal Policy Optimization with High-order Sampling Coverage), an exploratory reinforcement learning framework that addresses mode collapse in Large Language Model fine-tuning by incentivizing discovery of low-similarity yet high-validity reasoning patterns. Evaluations on GSM8K, SVAMP, and code generation tasks show PPO-HSC enhances solution diversity and state-space coverage while maintaining or surpassing accuracy of state-of-the-art RL baselines.", "body_md": "arXiv:2607.16206v1 Announce Type: new\nAbstract: This paper introduces PPO-HSC (Proximal Policy Optimization with High-order Sampling Coverage), an exploratory reinforcement learning framework designed to address the \"Invisible Shackles\" of mode collapse in Large Language Model (LLM) fine-tuning. While standard Reinforcement Learning from Verifiable Rewards (RLVR) effectively reinforces high-reward trajectories, it often leads models to over-optimize known solutions, sacrificing curiosity and the ability to explore broader solution manifolds. To overcome this, PPO-HSC incorporates a High-order Sampling Coverage (HSC) reward that incentivizes the discovery of \"low-similarity yet high-validity\" reasoning patterns. By maintaining a dynamic trajectory library of verified unique solutions, the framework provides a differentiable signal that rewards semantic novelty while ensuring structural rationality through a plausibility constraint. Empirical evaluations on mathematical reasoning (GSM8K, SVAMP) and code generation tasks demonstrate that PPO-HSC significantly enhances solution diversity and state-space coverage while maintaining or surpassing the accuracy and syntax integrity of state-of-the-art RL baselines.", "url": "https://wpnews.pro/news/ppo-hsc-an-exploratory-reinforcement-learning-framework-based-on-wide-area", "canonical_source": "https://arxiv.org/abs/2607.16206", "published_at": "2026-07-21 04:00:00+00:00", "updated_at": "2026-07-21 04:06:56.201929+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research"], "entities": ["PPO-HSC", "Proximal Policy Optimization with High-order Sampling Coverage", "Large Language Model", "RLVR", "GSM8K", "SVAMP"], "alternates": {"html": "https://wpnews.pro/news/ppo-hsc-an-exploratory-reinforcement-learning-framework-based-on-wide-area", "markdown": "https://wpnews.pro/news/ppo-hsc-an-exploratory-reinforcement-learning-framework-based-on-wide-area.md", "text": "https://wpnews.pro/news/ppo-hsc-an-exploratory-reinforcement-learning-framework-based-on-wide-area.txt", "jsonld": "https://wpnews.pro/news/ppo-hsc-an-exploratory-reinforcement-learning-framework-based-on-wide-area.jsonld"}}