aiqbench analysis
This research introduces a Hessian-free bilevel reinforcement learning algorithm that achieves state-of-the-art sample complexity by leveraging the optimality of Boltzmann policies in entropy-regularized settings. By removing restrictive assumptions like the Polyak-Lojasiewicz condition, it offers improved scalability for meta-learning and RLHF applications without relying on computationally expensive second-order derivatives.
Hypergradient-based Bilevel Reinforcement Learning with Improved Sample Complexity
arXiv:2607.28849v1 Announce Type: new Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2607.28849 [cs.LG]
(or arXiv:2607.28849v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.28849 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 239 words
Related coverage
Reward-Aligned Distillation
arXiv · Oct 14, 2026
Enabling Language-specific Reasoning in Multilingual Models with Reinforcement Learning
AMD · Jul 24, 2026
Granite 4.1 LLMs: How They’re Built
Hugging Face · Apr 29, 2026
In open RLVR, “improvement” depends on the instrument — a small GRPO testbed separating what training optimizes, measures, and teaches
LessWrong · Jun 15, 2026