arXiv:2609.10052v1 Announce Type: new
Abstract: LLM agents for sequential decision tasks are often post-trained with trajectory-level outcome labels, but such labels provide little supervision for pr...
By Junwon Ko, Dong-Jae Lee, Minchan Kwon, Sunghyun Baek, Junmo Kim
arXiv:2608. 13297v1 Announce Type: new Abstract: In evolutionary search, a weak child can be a valuable ancestor that makes high-fitness regions reachable.
By Matthew Siper, Ahmed Khalifa, Julian Togelius
The paper introduces RePPO, a method that adapts Proximal Policy Optimization to jointly optimize a compact population of diverse, high‑quality policies using a population‑dependent, dynamically shaped reward. RePPO achieves a strong quality‑diversity tradeoff on standard continuous‑control tasks and scales to larger policy and behavior spaces, maintaining quality while significantly increasing diversity in pixel‑based tasks. The authors also demonstrate RePPO’s applicability to fine‑tuning large language models, showing for the first time that quality‑diversity reinforcement learning can scale to this domain.
By Saeed Hedayatian, Harsh Shah, Celine Mneimneh, Stefanos Nikolaidis
arXiv:2601. 12401v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning large-scale generative models, such as diffusion and flow models, to align with complex human preferences and user-specified tasks.
By Jinmei Liu, Haoru Li, Zhenhong Sun, Chaofeng Chen, Yatao Bian, Bo Wang, Daoyi Dong, Chunlin Chen, Zhi Wang
arXiv:2611.05929v1 Announce Type: cross
Abstract: Off-policy deep reinforcement learning trains on random samples from a replay buffer of past experience. In physical robot settings, that buffer is d...
By Shaifalee Saxena, Alexander Scheinker
SwiftExplorer is a training‑free diffusion model alignment plugin that addresses two key issues in objective‑guided sampling: the loss of diversity due to strong directional bias and the inefficiency of constant guidance. It introduces an Inheritance‑Restart exploration mechanism to prevent early convergence and enhance the likelihood of high‑reward trajectories, while a Quality‑Efficiency arbitration mechanism removes incorrect signals and dynamically stops generation when optimal reward gain is achieved. Experiments show that SwiftExplorer improves preference, fidelity, diversity, and richness across multiple evaluation metrics.
By Renye Yan, Jikang Cheng, You Wu, Bojin Huang, Wei Peng, Zongwei Wang, Ling Liang, Yimao Cai