Reinforcement learning

Policy optimisation, reward modelling and RLHF — how models are trained by feedback rather than by labels.

6,934 stories · RSS feed

arXiv AI

Sep 10

Inference-Time Graph Engineering for Multi-Agent LLM Workflows

arXiv:2609.05774v1 Announce Type: new Abstract: Recent multi-agent LLM systems increasingly rely on graph-structured communication to coordinate specialized agents. We revisit multi-agent orchestrati...

By Katherine Tieu, Dongqi Fu, Yinglong Xia, Hong Li, Hong Yan, Jingrui He

llms agents reinforcement-learning

arXiv AI

Sep 10

SRPO: Setwise Relative Policy Optimization for Multi-Agent LLMs

arXiv:2609.08452v1 Announce Type: new Abstract: Multi-agent large language models solve complex tasks by coordinating several policies in a shared environment. However, existing reinforcement learnin...

By Shengtian Yang, Ziyu Xiong, Yu Li, Yewen Li, Qingpeng Cai, Lei Feng

llms agents reinforcement-learning

arXiv AI

Sep 10

The Surprising Effectiveness of Approximate Value Iteration in Self-Play

arXiv:2609.09094v1 Announce Type: new Abstract: Combining search with function approximation has driven major advances in game-playing programs, making self-play algorithms more competitive than ever...

By Raphael Boige, Amine Boumaza, Bruno Scherrer

reinforcement-learning

arXiv AI

Sep 10

On BatchNorm Forward Modes in Value-Based Reinforcement Learning

arXiv:2609.06421v1 Announce Type: cross Abstract: Batch normalization (BN) substantially improves sample efficiency in continuous-control actor-critic methods such as CrossQ, yet recent studies repor...

By Daniel Palenicek, Mikael Henaff, Scott Fujimoto, Koustuv Sinha

reinforcement-learning

arXiv AI

Sep 10

From LLM-Generated Specifications to Learned Quadruped Locomotion

arXiv:2609.07111v1 Announce Type: cross Abstract: Quadruped robot locomotion policies are often trained using reinforcement learning, which in turn relies heavily on hand-crafted reward functions. De...

By Merve Atasever, Keyan Azbijari, Cagan Bakirci, Alfredo Reina Corona, Tolga Izdas, Richard Yang, Erdem Biyik, Jyotirmoy V. Deshmukh

llms reinforcement-learning robotics safety

arXiv AI

Sep 10

Zero-Shot Sim-to-Real Contact-Rich Assembly via Proprioception-Anchored Cross-Modal Pretraining

arXiv:2609.07534v1 Announce Type: cross Abstract: Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sustained c...

By Yuhan Wang, Yurou Chen, Hongye Jiang, Wenzhao Lian

reinforcement-learning robotics fine-tuning multimodal safety

arXiv AI

Sep 10

FATS: A Prompt Injection Attack Utilizing Feign Security Agents with Deceptive Few-shots Learning

arXiv:2410.08776v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) face significant security risks despite their advanced capabilities. While techniques like Reinforcement Learnin...

By Yupeng Ren, Jiangtao Chen, Rui Zhang

llms agents reinforcement-learning benchmarks safety

arXiv AI

Sep 10

Q-Guided Stein Variational Model Predictive Control via RL-informed Policy Prior

arXiv:2507.06625v4 Announce Type: replace-cross Abstract: Model Predictive Control (MPC) enables reliable trajectory optimization under dynamics constraints, but often depends on accurate dynamics mo...

By Shizhe Cai, Zeya Yin, Jayadeep Jacob, Fabio Ramos

reinforcement-learning robotics

arXiv Machine Learning

Sep 10

Endogenous Exploration in Reinforcement Learning with Intrinsic Curiosity

arXiv:2609.05650v1 Announce Type: new Abstract: We propose a reinforcement learning framework in which exploration is driven by intrinsic curiosity, designed for scenarios where environments are non-...

By Armando Vieira

agents reinforcement-learning benchmarks

arXiv Machine Learning

Sep 10

Model-Adaptive and Risk-Constrained Frequency Hopping Against Predictive Jammers

arXiv:2609.06514v1 Announce Type: new Abstract: Adaptive frequency hopping against predictive jamming must address both model uncertainty and policy exposure: the context-loss relationship may vary a...

By Yanbo Chen, Xinjing Zhou

reinforcement-learning safety

arXiv Machine Learning

Sep 10

Behavioral Cloning Outperforms Entropy-Regularized RL: Critic-Driven Failure of Actor-Critic Methods on Adaptive Tumor Treatment

arXiv:2609.06667v1 Announce Type: new Abstract: Adaptive dosing requires policies that reduce tumor burden without excessive toxicity. Learned dosing policies are typically judged against historical...

By Aleksandar Dimitrov, Giacomo Spigler

reinforcement-learning fine-tuning safety

arXiv Machine Learning

Sep 10

Temporal-Causal Inference for Reinforcement Learning via Automata Learning

arXiv:2609.07461v1 Announce Type: new Abstract: We consider reinforcement learning in environments with dynamics that undergo an irreversible phase transition governed by a hidden temporal pattern. T...

By Jan Corazza, Daniil Kaminskyi, Simon Lutz, Patrick Nossol, Hadi Partovi Aria, Zhe Xu, Daniel Neider

agents reinforcement-learning

arXiv Machine Learning

Sep 10

Emergent Charging Coordination in Electric Delivery Fleets

arXiv:2609.07689v1 Announce Type: new Abstract: In electric delivery fleets, mid-shift charging is non-trivial: each vehicle must decide when, where and how much to charge to finish on time with batt...

By Javier Vales-Alonso, Juan J. Alcaraz

agents reinforcement-learning safety

arXiv Machine Learning

Sep 10

Eliciting Weak-to-Strong Generalization with On-Policy Reverse Distillation

arXiv:2609.08798v1 Announce Type: new Abstract: Weak-to-strong generalization asks whether stronger models can learn from weaker supervisors and surpass them. This question is particularly important...

By Youngrok Park, Sangmin Bae, Hojung Jung, Jongwoo Ko, Yunseon Choi, Young Jin Kim, Pashmina Cameron, Aaron Courville, Se-Young Yun

reinforcement-learning efficiency

arXiv Machine Learning

Sep 10

PlayTrain: An Efficient Reinforcement Learning Framework for LLM-Generated Adaptable JavaScript Games

arXiv:2609.09059v1 Announce Type: new Abstract: While many video-game environments (VGEs) have played crucial roles in advancing reinforcement learning (RL), developing novel VGEs or modifying existi...

By Ryan Truong, Lance Ying, Samuel J. Gershman, Kazuki Irie

llms agents reinforcement-learning

arXiv Machine Learning

Sep 10

Towards Bridging the Gap Between Offline and Iterative Alignment via Preference Distillation

arXiv:2609.06893v1 Announce Type: cross Abstract: Direct preference optimization DPO is a promising offline approach for aligning large language models (LLMs) due to its simplicity, computational eff...

By Wenbo Zhang, Wenzhuo Zhou, Hengrui Cai, Zhengling Qi

llms reinforcement-learning efficiency benchmarks safety

arXiv Machine Learning

Sep 10

Continual Policy Consolidation for Lifelong Robot Learning

arXiv:2601.22475v2 Announce Type: replace Abstract: Building a generalist robot policy requires continuously integrating new skills while preserving previously acquired behaviors. Directly optimizing...

By Qijun He, Yuxuan Li, Mingqi Yuan, Xiaoquan Sun, Wen-Tse Chen, Jeff Schneider, Jiayu Chen

llms reinforcement-learning robotics efficiency

arXiv Machine Learning

Sep 10

Temporal Consistency Improves Generalization in Contextual Offline Meta Reinforcement Learning

arXiv:2603.02935v2 Announce Type: replace Abstract: Offline meta-reinforcement learning seeks to learn a policy that generalizes to new related tasks online. Context-based methods infer a task repres...

By Mohammadreza Nakheai, Aidan Scannell, Kevin Luck, Joni Pajarinen

diffusion reinforcement-learning benchmarks

arXiv Machine Learning

Sep 10

Apriel-Reasoner: RL Post-Training for General-Purpose and Efficient Reasoning

arXiv:2604.02007v3 Announce Type: replace Abstract: Building general-purpose reasoning models using reinforcement learning with verifiable rewards (RLVR) across diverse domains has been widely adopte...

By Rafael Pardinas, Ehsan Kamalloo, David Vazquez, Alexandre Drouin

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Sep 10

Towards Near-Real-Time Telemetry-Aware Routing with Neural Routing Algorithms

arXiv:2604.02927v3 Announce Type: replace Abstract: Routing algorithms are crucial for efficient computer network operations, and in many settings they must be able to react to traffic bursts within...

By Andreas Boltres, Niklas Freymuth, Benjamin Schichtholz, Michael K\"onig, Gerhard Neumann

reinforcement-learning

Get the daily briefing