arXiv Machine Learning July 13, 2026 By Alan Nadelsticher Ruvalcaba

Evolutionary Discovery of Developmental Reward Schedules in Deep Reinforcement Learning

Read the original on arXiv Machine Learning →

arXiv:2606. 20858v2 Announce Type: replace Abstract: The temporal structure of reward composition in reinforcement learning (RL) is typically hand-designed and held fixed throughout training, leaving the progression of motivational priorities largely unexplored.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

reinforcement-learning

arXiv Machine Learning

Aug 6

Reward Structure Shapes the Interaction Between Episodic Exploration and Neural Memory in Reinforcement Learning

arXiv:2608. 05111v1 Announce Type: new Abstract: In partially observable reinforcement learning, agents face a dual bottleneck: they must explore to encounter rewarding states and retain that experience in memory to optimize their policies.

By Jai Malegaonkar, Rohan Patil, Henrik I. Christensen

agents reinforcement-learning efficiency

arXiv Machine Learning

Jul 10

Beyond Success Rates: Trainability and Extractability for Offline GCRL

arXiv:2602. 05459v2 Announce Type: replace Abstract: Offline goal-conditioned reinforcement learning (GCRL) is typically benchmarked by the best tuned success rate of each method.

By Jan Malte T\"opperwien, Aditya Mohan, Marius Lindauer

reinforcement-learning robotics

arXiv AI

Aug 11

A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning

arXiv:2608. 08158v1 Announce Type: new Abstract: Sparse, delayed, and weakly informative rewards remain central obstacles to efficient reinforcement learning.

By Fouad Bahrpeyma

reinforcement-learning safety

arXiv Machine Learning

Aug 5

Latent Reward Registers for Diffusion Preference Alignment

arXiv:2608. 03929v1 Announce Type: new Abstract: Aligning diffusion models with human preferences usually relies on a sparse terminal reward evaluated on the final generated samples, presenting a severe temporal credit-assignment challenge across the multi-step denoising process.

By Yuanshen Guan, Zipeng Feng, Zhiwei Xiong, Peiqin Sun

llms diffusion reinforcement-learning efficiency benchmarks safety

arXiv Machine Learning

Jul 27

Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning

arXiv:2607. 21971v1 Announce Type: new Abstract: Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains.

By Shujin Wu, Cheng Qian, Xiusi Chen, Heng Ji

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 10

Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning

arXiv:2606. 10129v1 Announce Type: new Abstract: While deep Reinforcement Learning (deep-RL) has been increasingly applied to parameter control in evolutionary algorithms, rigorous theoretical analysis of parameter control remains largely restricted to single-parameter settings, owing to the difficulty of deriving effective, interpretable multi-parameter policies amenable to formal study.

By Tai Nguyen, Phong Le, Carola Doerr, Nguyen Dang

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Reward Structure Shapes the Interaction Between Episodic Exploration and Neural Memory in Reinforcement Learning

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