The paper introduces a new class of Bayesian neural networks that place Dirichlet Process priors on the data-generating distribution instead of on network weights, aiming to better capture epistemic uncertainty. Using this framework, the authors develop DP-DQN, a reinforcement learning algorithm that maintains posterior distributions over value functions and performs efficient Thompson sampling. DP-DQN combines data-driven posterior reweighting via the Bayesian Bootstrap with generative prior hypotheses from the DP base measure, enabling deep exploration with a single neural network without requiring multi-network ensembles.
By Sumit Vashishtha
AdaDex is a goal‑conditioned reinforcement learning framework that transforms a short hand‑object trajectory—captured via mocap or in‑the‑wild RGB(D) video—into an adaptable dexterous skill. It trains a shared policy on two parallel environment populations: motion‑tracking environments that embed human priors and generalization environments that enforce robustness to noisy states and goals. The resulting policies achieve diverse, functional dexterous behaviors such as grasping, re‑posing, in‑hand reorientation, and dynamic throw‑and‑catch, outperforming baselines in generalization and demonstrating transfer from simulation to the real world.
By Yuxuan Kuang, Jiashun Wang, Yixuan Sun, Yufei Ye, Sungjae Park, Umar Iqbal, Katerina Fragkiadaki, Shubham Tulsiani
The paper introduces a reinforcement learning framework that uses large language models to generate RTL circuits while optimizing for Pareto-optimality across power, performance, and area (PPA). It proposes a generation-based training pipeline and a Hypervolume Contribution reward to improve both the quality and diversity of the entire set of generated circuits. Experiments on the RTLLM v2 benchmark show significant gains in hypervolume and diversity compared to baseline and single-objective methods.
By Koki Takeshita, Kenji Uebayashi, Hikaru Tomonari, Masaya Tsunokake, Koichiro Yawata, Tatsuya Sasaki, Yusuke Ohtsubo, Kota Dohi, Yasuhiro Sogawa
The paper investigates how optimizing the harness of language model agents—without changing model weights—affects performance. It distinguishes between gains that improve success on tasks already within reach (elicitation) and gains that broaden the range of solvable tasks (expansion). By comparing optimized harnesses to their pre‑optimization versions across static and dynamic tasks, the study finds that benefits can reverse at higher attempt budgets and that aggregate gains may mask shortcomings on harder tasks, highlighting the need for detailed diagnostics.
By Younghyun Kim, Wooseok Seo, Minseo Kim, Seunghyuk Oh, Minjae Lee, Donghoon Kim, Hyung Il Koo, Jaehyung Kim, Wonjun Kang
arXiv:2610.27895v1 Announce Type: cross
Abstract: Direct preference optimisation (DPO) is widely used for language model post-training, but its performance depends strongly on the choice of its hyper...
By Hannan Mahadik, David Salinas, Jacek Golebiowski, Aaron Klein
arXiv:2610.27214v1 Announce Type: new
Abstract: Estimating extreme risk in reinforcement learning becomes increasingly difficult as the confidence level increases, since only a small number of observ...
By Carlos P\'erez Octavio Mendoza, Fr\'ed\'eric Godin
arXiv:2610.28010v1 Announce Type: new
Abstract: The integration of Large Language Models and Reinforcement Learning offers a promising direction for complex sequential decision-making tasks, such as...
By Artem Mikhaylov, Pavel Konoplev, Nikolay Efanov, Artyom Sorokin, Evgeny Burnaev
arXiv:2610.28136v1 Announce Type: cross
Abstract: Gaussian process upper confidence bound (GP-UCB) has been widely studied, though its known regret upper bound is suboptimal. To obtain near-optimal r...
By Shion Takeno, Shogo Iwazaki, Ichiro Takeuchi
arXiv:2510.03811v2 Announce Type: replace
Abstract: We develop an approach for the de novo synthesis of messenger RNA (mRNA) sequences encoding target proteins. This requires optimizing multiple biol...
By Aya Laajil, Abduragim Shtanchaev, Sajan Muhammad, Aya El Mir, Eric Moulines, Tiago Silva, Salem Lahlou
arXiv:2607.08925v2 Announce Type: replace
Abstract: Training agents directly on the task they will be deployed on often leads to the best policy performance, but training on physical robots can cause...
By Elham Daneshmand, Majid Khadiv, Glen Berseth, Hsiu-Chin Lin
arXiv:2609.33848v2 Announce Type: replace
Abstract: Large language model (LLM) agents increasingly undertake extreme-long (xlong) horizon tasks, where a single execution can span hours, hundreds of m...
By Weiqi Wang, Yuxin Zhou, Mouxiang Chen, Siyuan Zhang, Yi Zhang, Yuyan Luo, Zhiyu Yin, Chencan Wu, Jiemin Jiang, Wentao Yao, Chujie Zheng, JianWei Zhang
arXiv:2610.05220v2 Announce Type: replace
Abstract: Carousel interfaces allow a recommender system to directly observe how far a user has browsed. This signal distinguishes displayed but unclicked it...
By Takuma Yasuda, Atsuyoshi Nakamura, Dai Hai Nguyen
arXiv:2509.14203v4 Announce Type: replace-cross
Abstract: Learning and optimal control under robust Markov decision processes (MDPs) have received increasing attention, yet most existing theory, algo...
By Shengbo Wang, Nian Si
arXiv:2610.27237v1 Announce Type: new
Abstract: We present a study on controlled automatic simplification of Russian texts using preference-based optimization algorithms with a focus on adapting text...
By Kristina Mustakova, Alexey Sorokin
arXiv:2504.19024v2 Announce Type: replace
Abstract: We propose a novel k-step return estimation method (called KETCHUP) for Reinforcement Learning(RL)-based knowledge distillation (KD) in text genera...
By Jiabin Fan, Guoqing Luo, Michael Bowling, Lili Mou
arXiv:2601.16400v2 Announce Type: replace
Abstract: Real-world visual question answering (VQA) may be context-dependent: an image-question pair could be under-specified, such that the correct answer...
By Zongwan Cao, Bingbing Wen, Lucy Lu Wang
The article discusses how glass-box deep reinforcement learning (DRL) can discover actuation strategies for high‑speed, nonlinear, unsteady flows without explicit models, potentially revealing mechanisms beyond classical control. It highlights the challenges of applying DRL to real high‑speed flows, including simulation cost, data efficiency, sensing in extreme environments, reward design, explainability, and generalization across Mach and Reynolds numbers. The authors argue that overcoming these constraints will require physics‑aware learning, robust real‑time operation, and extensive validation to translate DRL capabilities into reliable, certified technology.
By Ameya D. Jagtap
Self-Credit is an agentic reinforcement learning method that transforms sparse outcome rewards into dense supervision signals through self-generated credit assignment and intra-group rescaling. The approach involves the policy model summarizing key evidence with citations, refining these citations by comparing actions across successful and failed rollouts, and reweighting advantages based on the rescaled credit. Experiments on BrowseComp-Plus, GAIA, and xBench-DeepSearch show significant performance gains over baseline models and other agentic RL methods.
By Fangcong Yin, Ming Yin, Tanvi Bhandarkar, Aashu Singh, Eryk Helenowski, Shlok Kumar Mishra, Saghar Hosseini, Karthik Abinav Sankararaman, Greg Durrett, Dinkar Juyal
arXiv:2602.00348v2 Announce Type: replace
Abstract: Metal implants in MRI cause severe artifacts that degrade image quality and hinder clinical diagnosis. Traditional approaches address metal artifac...
By Zhengyi Lu, Ming Lu, Chongyu Qu, Junchao Zhu, Junlin Guo, Marilyn Lionts, Yanfan Zhu, Yuechen Yang, Tianyuan Yao, Jayasai Rajagopal, Bennett Allan Landman, Xiao Wang, Xinqiang Yan, Yuankai Huo
The paper introduces LENS, a tool for black‑box performance analysis of large language model (LLM)–based network control systems. LENS extends deep reinforcement learning to identify performance gaps and challenging inputs for LLMs, revealing significant deficiencies in LLM‑driven traffic engineering and GPU allocation. It demonstrates that LENS can uncover gaps comparable to those found by white‑box analyzers for hand‑crafted heuristics.
By Mahdi Alizadeh, Pooria Namyar, Arvin Ghavidel, Ramesh Govindan