{"slug": "hypergradient-based-bilevel-reinforcement-learning-with-improved-sample", "title": "Hypergradient-based Bilevel Reinforcement Learning with Improved Sample Complexity", "summary": "Researchers propose a Hessian-free hypergradient-based bilevel reinforcement learning algorithm that achieves an iteration complexity of O(ε⁻¹) and state-of-the-art sample complexity of Õ(ε⁻²) under mild regularity conditions, removing the Polyak-Lojasiewicz condition assumption on the outer-level objective function. The method leverages the optimality of the Boltzmann policy for entropy-regularized discounted RL objectives, addressing scalability and sample complexity issues in bilevel RL frameworks such as meta-learning and RL from human feedback.", "body_md": "arXiv:2607.28849v1 Announce Type: new\nAbstract: Bilevel reinforcement learning (RL) is an important framework within the literature of RL that can be used to formalize various categories of problems, such as meta-learning, hierarchical task decomposition, and reinforcement learning from human feedback (RL-HF). Most of the bilevel RL algorithms are either not scalable because of using hypergradient with Hessian, or they suffer from high sample complexity because of using penalty-based approximation methods. In this work, we propose a hypergradient-based bilevel RL algorithm using the optimality of the Boltzmann policy for the entropy regularized discounted RL objective function. Our proposed algorithm is Hessian-free and obtains an iteration complexity of $O(\\epsilon^{-1})$ and state-of-the-art sample complexity of $\\tilde{O}(\\epsilon^{-2})$ under mild regularity conditions. Further, in our convergence analysis, we are able to remove the assumption of the Polyak-Lojasiewicz (PL) condition on the outer-level objective function present in the prior state-of-the-art sample complexity work.", "url": "https://wpnews.pro/news/hypergradient-based-bilevel-reinforcement-learning-with-improved-sample", "canonical_source": "https://arxiv.org/abs/2607.28849", "published_at": "2026-08-03 04:00:00+00:00", "updated_at": "2026-08-03 04:03:19.405459+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/hypergradient-based-bilevel-reinforcement-learning-with-improved-sample", "markdown": "https://wpnews.pro/news/hypergradient-based-bilevel-reinforcement-learning-with-improved-sample.md", "text": "https://wpnews.pro/news/hypergradient-based-bilevel-reinforcement-learning-with-improved-sample.txt", "jsonld": "https://wpnews.pro/news/hypergradient-based-bilevel-reinforcement-learning-with-improved-sample.jsonld"}}