Reinforcement Learning in Robotics

Últimos artículos

• Make Sparse Rewards Count: Density-Aware Reward Aggregation for Multi-Reward RL

  Tong Zheng, Skylar Zhai, Zhan Cheng, TianMing Sha, Youling Huang, Shuo Zhou, Shaotong Qi, Jingcheng Liang, Xuwei Ding, Pengcheng Xu · 2 de octubre de 2026

  Multi-reward reinforcement learning trains large language models to satisfy multiple behavioral objectives simultaneously. Reward-wise normalization, as used in GDPO, preserves reward-specific relative information within rollout groups, but different objectives can still exhibit uneven learning prog…

• Probe with Participation Trophies: Random-Reward RL as a Probe of LLM Capability

  Yu Mao, Lei Yu, Zining Zhu, Yusheng Zheng, Haohang Li, Freda Shi, Yutong Yin, Zhaoran Wang, Jingcheng Niu · 2 de octubre de 2026

  We connect the spurious-reward paradox to a model's reachability and propose random-reward reinforcement learning (RL) as a useful tool for the probing enterprise, addressing a decade-long debate over what probing performance actually reveals about a model. There are two prevailing explanations for …

• FERPO: Forward Entropy-Regularized Policy Optimization

  Sebastian Sanokowski, Alireza Sarmadi, Majid Khadiv · 2 de octubre de 2026

  Several state-of-the-art methods for online reinforcement learning in continuous control improve policies using action gradients of a learned critic. However, critics are typically trained to predict returns, and accurate value predictions do not necessarily yield accurate action derivatives, potent…

• Flowing Faster to Coordinate: One-Step Online Multi-Agent Flow Policies

  Zhuoran Li, Yunzhan Li, Xun Wang, Yihan Du, Longbo Huang · 2 de octubre de 2026

  Multi-agent reinforcement learning (MARL) provides a powerful framework for learning coordinated behaviors through interactions with the environment. Developing MARL policies requires balancing expressive modeling of complex and multimodal action distributions with efficient training and execution. …

• iADD: Improving Alignment and Diversity in Diffusion Policy Optimization

  Ashok Prasad Neupane, Saugat Adhikari, Pramish Paudel, Ajad Chhatkuli, Danda Pani Paudel · 2 de octubre de 2026

  Reinforcement learning based post training of diffusion models, such as Denoising Diffusion Policy Optimization (DDPO), optimizes a reverse diffusion process under a reward function. However, current approaches to reward optimizations do so at the cost of diversity and quality. In this paper, we pro…

• Iterative Policy Refinement through Semantic Rollout Analysis

  Feiyu Gavin Zhu, Qi Xu, Zhifei Deng, Zhigang Hua, Luke Simon, Jean Oh, Reid Simmons · 2 de octubre de 2026

  Structured policies improve efficiency, robustness, and interpretability in imitation learning by introducing task-specific inductive bias, but existing structure generation methods rely either on extensive human input or on static domain knowledge encoded in LLMs, which may be inconsistent with the…

• Optimal Transport Meets Reinforcement Learning: A Survey

  Yujie Zhu, Charles A. Hepburn, Matthew Thorpe, Giovanni Montana · 2 de octubre de 2026

  Reinforcement learning (RL) algorithms frequently compare probability distributions, such as state visitation distributions induced by policies and experts, action distributions from learned policies and offline datasets, or transition distributions from learned models and environments. However, com…

• Does Scaling Reinforcement Learning Really Require More Training?

  Bangji Yang, Jiajun Fan, Hongba Ma, Ruihan Guo, Ge Liu · 2 de octubre de 2026

  Scaling reasoning typically spends more compute on reinforcement learning (RL) or on inference. We show that a completed RL training history can yield policies stronger than the checkpoints visited by its optimizer. We call this policy-space scaling: expanding the deployable policy set accessible fr…

• SHARPO: Segment-Level Credit Assignment for Agentic Reinforcement Learning

  Xinchen Du, Zhengze Zhou, Wenhui Zhu, Han Yu, Sen Na, Rohit Jain, Alborz Geramifard · 2 de octubre de 2026

  Agentic reinforcement learning (RL) trains a large language model (LLM) to act over long, multi-step interactions. However, a single localized error can cause task failure, while trajectory-level rewards provide limited guidance for assigning credit to individual decisions. To address this limitatio…

• Learning Transferable Skills using Goal-Conditioned Bisimulation

  Mohammad Amin Abbasfar, Farbod Azimmohseni, Mohammad Hossein Rohban · 2 de octubre de 2026

  Unsupervised skill discovery has emerged as a promising approach for leveraging reward-free datasets to pretrain general-purpose policies. However, current skill discovery methods either require access to expert data or exhibit limited generalization, failing to transfer effectively to previously un…

• Exploring More, Reasoning Better: Stepwise Risk-Sensitive GRPO for Diffusion Language Models

  Yue YU, Bowen Zuo, David Crandall, Yinglun Zhu, Dongruo Zhou · 2 de octubre de 2026

  Diffusion large language models (dLLMs) generate text by denoising a sequence or successive blocks, allowing several tokens to be revealed in parallel. Reinforcement learning with verifiable rewards (RLVR) reuses terminal feedback across these decisions, even as their conditioning context changes. W…

• ALER: Adaptive Learnable Experience Rewriting for Reinforcement Learning

  Oleg Shchendrigin, Egor Cherepanov, Aleksandr I. Panov, Alexey K. Kovalev · 2 de octubre de 2026

  In partially observable reinforcement learning (RL), a later observation can make stored information obsolete or change what it implies for the next decision. Memory architectures and benchmarks for RL mostly test retention, the ability to keep information unchanged until it is needed. We formalize …

• T2SPO: Trajectory-to-Step Policy Optimization for Agentic Reinforcement Learning

  Bo-Wen Zhang, Junwei He, Maoqi Liu, Feiran Li, Song-Lin Lv, Wentao Ma, Rongyi Lin, Shuhan Zhong, Lan-Zhe Guo · 2 de octubre de 2026

  Reinforcement learning enables large language model (LLM) agents to learn multi-step behaviors through interaction with their environments. However, rewards in many interactive tasks reflect only the final outcome, providing limited guidance on which intermediate decisions advance the task. Successf…

• DriftOPD: Sequence-Level Reverse-KL Distillation for One-Step VLA Policies

  Youngjun Jun, Kyumin Choi, Youngmin Kim, Seonghyun Jin, Sunwoo Park, Jangho Park, Jong Chul Ye · 2 de octubre de 2026

  Vision-Language-Action (VLA) models increasingly rely on action experts that generate short action chunks under receding-horizon control. While chunk-level training is convenient across robot embodiments, it optimizes local action likelihood without explicitly accounting for long-horizon task succes…

• Not All Experience Belongs in the Weights: Component Routing for Self-Improving GUI Agents

  Beining Wu, Zihao Ding, Jun Huang · 2 de octubre de 2026

  Self-improving GUI agents keep the trajectories they produce and return them to the agent, by fine-tuning or by retrieval into the prompt, and studies that compare the two destinations disagree. We attribute this to the unit of experience: a trajectory bundles items with different properties, so a c…

• Q-Learning for Reachability in MEC-Free MDPs

  Lu-Chin Chang, Suguman Bansal · 2 de octubre de 2026

  Reinforcement learning (RL) for reachability specifications is fundamental to sequential decision-making. Prior work establishes asymptotic convergence to optimal policies, but only through model-based methods that must explicitly estimate the transition probabilities of the underlying Markov Decisi…

• Measuring the Stability Assumption Behind Action Chunking

  Aryan Goyal · 2 de octubre de 2026

  Action chunking improves the performance of policies learned by behavioural cloning, and several mechanisms have been proposed to explain why, including temporal consistency, horizon reduction, representation learning, and reduced error compounding. We instead study what happens to an action error o…

• Decision Titan: Test-Time Training for Long-Term Memory in Offline Reinforcement Learning

  Jude Waide, Robert Lieck · 2 de octubre de 2026

  Long-term dependencies remain a major challenge for sequential decision-making in the field of AI: RNNs suffer from vanishing gradients and the limited expressivity of vector-based hidden states, whilst Transformer-based models are limited by the quadratic scaling of attention. Recent work has propo…

• Rethinking Probability-Based Reinforcement Learning From Posterior Concentration

  Shiu-Hong Kao, Yubo Zhao, Zhenyu Tian, Pengzhan Sun, Yicong Li, Angela Yao · 2 de octubre de 2026

  Verifier-free reinforcement learning with probability-based rewards offers a promising way to train LLMs on general reasoning tasks where external verifiers are unavailable. Yet the reliability of these rewards, especially in long-horizon reasoning, remains underexplored. This work identifies a leng…

• Dependency-Aware Reward Shaping for Agentic Reinforcement Learning

  Ziyi Chen, Yan Zhang, Jianhui Wei, Daoan Zhang, Zuozhu Liu · 2 de octubre de 2026

  When training large language models with reinforcement learning, terminal rewards provide little guidance about which steps matter. Common methods for assigning step credit overlook that work built on uncorrected mistakes is wasted while independent work remains valid. With only a final success/fail…

• Calibration-risk routing for controlled world-model adaptation

  Yifan Zhang, Liang Zheng · 2 de octubre de 2026

  Model-based reinforcement learning (MBRL) can exploit simulated experience, but a simulator-to-target shift creates a model-selection problem: correcting the simulator and fitting the target directly can each fail under limited target data. We introduce the Model-Corrected World Model (MC-WM), which…

• PG-SFT: Balancing Capability Acquisition and Retention in Offline Agent Fine-Tuning

  Ronghua Li, Zi Liang, Zhishan Li, Shinan Liu · 2 de octubre de 2026

  Supervised fine-tuning (SFT) on offline agent trajectories is the standard approach for training specialized tool-using agents, but forcing models to imitate reasoning and actions token by token may harm other capabilities (e.g., general reasoning, tool calling, code generation) of the base model. I…

• Learning Multiple Timescales for Goal-Conditioned Reinforcement Learning

  Pedro Robles Dutenhefner, Dikshant Shehmar, Wagner Meira Jr., Marlos C. Machado · 2 de octubre de 2026

  Existing approaches to offline goal-conditioned reinforcement learning (GCRL) struggle with long-horizon tasks. Discounting shrinks value differences between distant states until they fall below the function approximation error, leaving the agent with no signal for ranking states. Temporal abstracti…

• Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving

  Huiwen Yan, Kyriakos G. Vamvoudakis, Mushuang Liu · 2 de octubre de 2026

  This paper develops a meta-multi-agent reinforcement learning (meta-MARL) framework to enable fast adaptation of interactive policies in a multi-agent system (MAS). Meta-reinforcement learning (meta-RL) enables agents to rapidly adapt to new tasks/environments using a bi-level optimization mechanism…

• TagPR: Tag-Guided Process Supervision for Personalization Reasoning in Large Language Models

  Song Jin, Juntian Zhang, Ruyu Lyu, Yong Liu, Xun Zhang, Yufei Zhang, Fei Jiang, Guojun Yin, Wei Lin, Rui Yan · 1 de octubre de 2026

  Recent advancements have endowed Large Language Models with impressive general reasoning capabilities. However, these reasoning models often perform worse than non-reasoning models on personalization tasks. While some methods use outcome-based RL to improve personalization reasoning, they fail to su…