{"slug": "not-all-transitions-matter-evidence-from-ppo", "title": "Not All Transitions Matter: Evidence from PPO", "summary": "Researchers found that removing 25% of transitions from reinforcement learning rollout data stabilizes PPO training by breaking repetitive gradient structures caused by causally chained states. The method, requiring only a single sampling step without modifying the core algorithm, matched vanilla PPO's reward performance across five environments while producing more consistent training dynamics in KL divergence, policy entropy, and value estimates. This discovery addresses hidden instability in on-policy reinforcement learning that reward curves alone rarely reveal.", "body_md": "arXiv:2605.24071v1 Announce Type: new\nAbstract: Training a reinforcement learning agent on-policy means collecting fresh experience at every update, and that experience comes with a hidden problem. Each state in a rollout is the direct output of the previous one, causally chained together by the agent's own actions. Because of this, consecutive transitions are never truly independent. They carry overlapping information, and the gradient signal the network receives ends up far more repetitive than the batch size suggests. The same directions get reinforced over and over, the value network struggles to keep up as the policy shifts, and training becomes quietly unstable in ways that reward curves alone rarely reveal.\nThis paper asks whether that redundancy can simply be removed. We show that randomly dropping a fixed fraction of transitions from the rollout, at the right stage so the reward signal stays intact, is enough to break the repetitive gradient structure and stabilize training. The change is minimal: one sampling step, no new components, no modification to the core algorithm, and it works with any PPO implementation. Across five environments of increasing difficulty, CartPole-v1, Acrobot-v1, LunarLander-v2, HalfCheetah-v5, and Hopper-v5, the method matches vanilla PPO on reward while producing more consistent training dynamics across KL divergence, policy entropy, and value estimates. Dropping 25% of transitions turns out to be the sweet spot: enough to disrupt the redundancy, not enough to thin the batch.", "url": "https://wpnews.pro/news/not-all-transitions-matter-evidence-from-ppo", "canonical_source": "https://arxiv.org/abs/2605.24071", "published_at": "2026-05-26 04:00:00+00:00", "updated_at": "2026-05-26 04:07:45.383635+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence", "ai-research", "neural-networks"], "entities": ["PPO", "CartPole-v1", "Acrobot-v1", "LunarLander-v2", "HalfCheetah-v5", "Hopper-v5"], "alternates": {"html": "https://wpnews.pro/news/not-all-transitions-matter-evidence-from-ppo", "markdown": "https://wpnews.pro/news/not-all-transitions-matter-evidence-from-ppo.md", "text": "https://wpnews.pro/news/not-all-transitions-matter-evidence-from-ppo.txt", "jsonld": "https://wpnews.pro/news/not-all-transitions-matter-evidence-from-ppo.jsonld"}}