#reinforcement-learning

40 curated events

papersTODAY 04:00 UTC

Refinement-Based Flow Policy Optimization for Online Reinforcement Learning

A new arXiv paper introduces Refinement-based Flow Policy Optimization, a method for using flow-based policies in online reinforcement learning. Standard flow matching needs samples from the target distribution, which is unavailable when the desired action distribution is only implicitly defined. The approach is presented as a refinement procedure that sidesteps this requirement in online RL settings.

reinforcement-learning/flow-matching/policy-optimization/arxiv

papersTODAY 04:00 UTC

GraMRAG combines graph memory and reinforcement learning for multi-agent RAG

A new arXiv paper introduces GraMRAG, a framework that coordinates multi-agent, multi-step reasoning using a graph-based memory structure trained with reinforcement learning. The authors argue that current multi-agent retrieval-augmented generation systems are limited in reasoning depth and memory organisation, and position the graph memory approach as a way to address those gaps. The work focuses on complex multimodal reasoning tasks.

agents/rag/reinforcement-learning/research

papersTODAY 04:00 UTC

Paper Proposes Scheduling Method for Agent RL Across Different Harnesses

A new arXiv preprint introduces HarnessBandit, a scheduling approach for reinforcement learning that trains language-model agents across multiple deployment harnesses at once. These harnesses vary in system prompts, tool schemas, control loops, and trajectory formats, causing the same model to behave inconsistently. The method jointly weighs which harnesses are learnable and which transfer well, aiming to improve robustness across interfaces.

agents/reinforcement-learning/research/arxiv

papersTODAY 04:00 UTC

UniCAR-RL targets fine-grained perception failures in multimodal math reasoning

A new arXiv paper introduces UniCAR-RL, a reinforcement learning approach aimed at improving how multimodal large language models handle math problems involving diagrams and figures. The authors argue that weak fine-grained visual perception leads models to hallucinate details early, which then causes errors to compound through the rest of the reasoning chain. The method is framed as improving perception before deeper reasoning steps are attempted.

multimodal/reinforcement-learning/visual-reasoning/mllm

papersTODAY 04:00 UTC

arXiv paper surveys evaluation metrics for safe reinforcement learning

A new arXiv preprint examines how researchers measure performance in safe reinforcement learning, where an agent must maximize reward while keeping cumulative cost under a defined limit. The authors argue that existing benchmarks and metrics do not fully capture safety performance, and propose a framework for comparing methods more consistently. The work is an announcement-only cross-listing and has not been peer reviewed.

safety/reinforcement-learning/benchmarks/research

papersTODAY 04:00 UTC

Paper Analyzes How Exploration Emerges in Policy Gradient RL Through Retried States

A revised arXiv paper examines why exploration helps in reinforcement learning, arguing it only pays off when agents revisit similar states repeatedly. The authors show that without such retries, a purely greedy policy would be optimal, and study how exploration behavior can emerge in policy gradient methods.

reinforcement-learning/exploration/research/policy-gradient

papersTODAY 04:00 UTC

Teacher-Guided Curriculum Boosts Data Efficiency in RLVR Training

A new arXiv paper addresses a known failure mode in reinforcement learning with verifiable rewards (RLVR), where training problems that are too hard for a model produce uniformly failed attempts and yield no learning signal. The authors propose a teacher-guided curriculum that sequences training data so the model encounters problems it can actually solve, making the process more data-efficient. The work targets mathematical reasoning in large language models and falls within the cs.CL area.

rlvr/reinforcement-learning/curriculum-learning/reasoning

papersTODAY 04:00 UTC

HISPO Method Targets Credit Assignment in RLVR for Long Reasoning Traces

A new arXiv paper introduces HISPO (Hierarchical Importance-Sampling Policy Optimization with Entropy-Derived Segments), a reinforcement learning approach aimed at reinforcement learning with verifiable rewards. The method addresses the difficulty of assigning credit across long solution traces by splitting them into segments derived from entropy, so that different parts of a model's mathematical reasoning receive appropriate weight during training. The work is a cross-listed submission on arXiv's machine learning category.

reinforcement-learning/llm-reasoning/credit-assignment/arxiv

papersTODAY 04:00 UTC

Hierarchical Q-Learning Routing Proposed for Multi-Charger Scheduling in Wireless Sensor Networks

A new arXiv preprint introduces HQARRF, a scheduling approach that combines hierarchical Q-learning with force-aware routing to coordinate multiple chargers in wireless rechargeable sensor networks. The method aims to balance sensor death risk, charger energy limits, travel costs, return-to-base feasibility and inter-charger coordination, which urgency-driven schedulers handle poorly. The work is categorized under machine learning research.

reinforcement-learning/scheduling/wireless-networks/research

papersTODAY 04:00 UTC

Paper proposes agent-controlled goal selection and termination in hierarchical RL

A new arXiv paper examines the agent-centric general value function (ACGVF) approach, which shifts two design choices from the environment or system designer to the learning agent itself. Under this construction, the agent decides both which goal to pursue and when to treat a goal as completed. The note builds on prior work by Tasse et al. (2026) on goal-based hierarchical reinforcement learning.

reinforcement-learning/agents/hierarchical-rl/research

papersTODAY 04:00 UTC

TryOnReward Uses Foveated Consistency to Fine-Tune Virtual Try-On Models

A new arXiv paper introduces TryOnReward, a reinforcement fine-tuning approach for virtual try-on systems that aims to align generated images with human preferences. The method builds a scoring function around foveated consistency, concentrating evaluation on the regions viewers focus on most. It is positioned as a way to optimize preference-oriented goals rather than relying only on standard reconstruction losses.

virtual-try-on/reinforcement-learning/fine-tuning/image-generation

papersTODAY 04:00 UTC

Paper studies reinforcement learning for stochastic control with unknown drift and rewards

A new arXiv preprint examines continuous-time stochastic control problems where both the drift coefficients and the running reward functions are not known in advance. The authors adopt an exploratory reinforcement learning approach to handle these missing model components, and they provide theoretical analysis along with algorithms and convergence results. The work targets settings that may be high-dimensional.

reinforcement-learning/research/stochastic-control/arxiv

papersTODAY 04:00 UTC

Paper Proposes Exploration-Guided Prompt Scaffolding for Multimodal RL Post-Training

A new arXiv paper argues that training prompts in online reinforcement learning vary widely in how useful they are to the current policy, with some already solved and others too hard to give a dependable learning signal. The authors propose an exploration-guided prompt scaffolding method that selects or structures prompts for multimodal reinforcement post-training so rollouts are better spent. The work appears in both the cs.AI and cs.LG listings as arXiv:2609.15051v1.

reinforcement-learning/multimodal/training-data/arxiv

papersTODAY 04:00 UTC

Graph Neural Algorithmic Reasoning Reframed as a Reinforcement Learning Problem

A revised arXiv paper argues that neural algorithmic reasoning, which typically trains networks to imitate classic algorithms via supervised learning, is limited by its reliance on post-processing to produce valid outputs. The authors propose reformulating the task as a reinforcement learning problem, aiming to let models build correct solutions directly rather than repairing them afterward. The work appears in the cs.LG and cs.AI listings as a replacement submission.

graph-neural-networks/reinforcement-learning/algorithmic-reasoning/arxiv

papersTODAY 04:00 UTC

LLM-Assisted Multi-Agent RL Framework Coordinates EV Charging, Stations and Grid

A new arXiv paper proposes combining large language models with multi-agent reinforcement learning to jointly optimize electric vehicle charging scheduling in public charging systems. The approach targets three competing goals at once: driver charging satisfaction, charging station profitability, and stability of the smart grid. It is positioned as a unified optimization method for connected EV infrastructure in IoT settings.

agents/reinforcement-learning/energy/llm

papersTODAY 04:00 UTC

Paper tests physics-based assumption in RL simulators and world models

A new arXiv preprint examines the widely held assumption that learned dynamics models which conform to underlying physics produce more accurate predictions. The authors recover exact polynomial invariants from trajectories and canonicalise them as a method for diagnosing faults in reinforcement learning simulators and world models. The work frames these invariants as a diagnostic tool for checking whether a model's learned dynamics actually respect physical structure.

reinforcement-learning/world-models/simulators/arxiv

papersTODAY 04:00 UTC

Reinforcement learning approach generates crystals with target symmetry and properties

A new arXiv preprint presents a reinforcement learning method for inverse design of crystalline materials that treats crystal symmetry as a constraint alongside desired physical properties. The authors argue that a good numerical property value is only meaningful when the structure's underlying symmetry is appropriate, so their approach generates candidates that satisfy both. The work targets functional materials discovery where symmetry-aware generation matters.

materials-science/reinforcement-learning/generative-models/arxiv

papersTODAY 04:00 UTC

Graph Attention-Driven Hierarchical Reinforcement Learning for Cloud Workflow Scheduling

A new arXiv paper proposes a hierarchical reinforcement learning method that uses graph attention to schedule workflows in cloud environments. The approach targets three competing goals at once: meeting deadlines, improving container utilization, and lowering energy use. It also accounts for unpredictable task runtimes, communication costs that depend on where tasks are placed, and the need to decide task assignment and container selection together.

reinforcement-learning/cloud-computing/scheduling/graph-neural-networks

papersTODAY 04:00 UTC

ReCAST: Reward Credit Assignment Across Timesteps for Online Diffusion RL

A new arXiv paper introduces ReCAST, a method for assigning credit to individual timesteps when fine-tuning diffusion models with reinforcement learning from multiple reward signals. The work separates how much each reward should influence training, based on user preference, from how informative that reward actually is at a given point in the generation process. This distinction is used to drive online diffusion RL more effectively.

diffusion-models/reinforcement-learning/reward-modeling/arxiv

papersTODAY 04:00 UTC

VGFM Method Adds Dense Value Guidance to Flow Matching for Robot Policies

A new arXiv preprint introduces VGFM, a technique that guides flow-matching generative models with dense value signals to produce more expressive robot control policies. The approach targets robot learning from large offline datasets, where multimodal action representations are needed to capture varied behaviors. It aims to improve policy expressiveness within this offline learning paradigm.

robotics/flow-matching/reinforcement-learning/research

papersTODAY 04:00 UTC

Compact Policies for Submodular MDPs via LP-Based Submodular Orienteering

A new arXiv paper introduces an approach for deriving strong yet compact action-selection policies in Markov Decision Processes whose value functions are submodular. The method builds on a linear-programming formulation of submodular orienteering, a problem where an agent must reach a set of targets under a budget. The authors argue this yields policies that are both effective and compact, relevant to reinforcement learning and operations research settings where repeated action choice is required.

mdps/reinforcement-learning/optimization/arxiv

papersTODAY 04:00 UTC

HiGFRL Combines Hierarchical Graph Fusion With Reinforcement Learning for Cloud Scheduling

A new arXiv paper introduces HiGFRL, a method that fuses hierarchical graph representations with reinforcement learning to schedule tasks with dependencies across heterogeneous cloud clusters. The authors target the difficulty of jointly handling DAG structure and multi-dimensional resource limits in online settings, which they say existing deep RL schedulers address only partially. The work is a preprint and has not yet been peer reviewed.

reinforcement-learning/scheduling/cloud-computing/graph-neural-networks

papersTODAY 04:00 UTC

Paper combines process supervision with outcome-based credit for agent RL

A new arXiv preprint addresses a weakness in outcome-based reinforcement learning for language-model agents: because the whole trajectory receives a single advantage signal, individual decisions get only coarse credit over long interaction sequences. The authors propose reconciling process supervision with outcome-based credit, drawing on on-policy self-distillation to produce finer-grained guidance. The work is presented as a revised submission and targets long-horizon agent training.

agents/reinforcement-learning/training/arxiv

papersTODAY 04:00 UTC

Linear Ensemble Sampling Retains Regret Guarantees With Smaller Ensembles

A new arXiv paper examines how few models are needed in ensemble sampling, a randomized-exploration method for sequential decision problems, while still preserving theoretical regret bounds. Prior results relied on ensembles larger than practical implementations typically use, leaving the minimum viable size unclear. The work analyzes this setting for linear models.

research/reinforcement-learning/bandits/ensembles

papersTODAY 04:00 UTC

Study Analyzes 160,000 Training Runs to Improve Offline Policy Learning Baselines

A new arXiv paper examines how reporting choices, hyperparameter tuning, and dataset characteristics affect offline policy learning results. Drawing on roughly 160,000 training runs, the authors argue that reliable progress requires careful reporting, well-tuned baselines, and evaluation across varied conditions. The work offers practical guidance for making policy-learning benchmarks more reproducible and comparable.

reinforcement-learning/benchmarks/reproducibility/hyperparameters

papersTODAY 04:00 UTC

Paper Finds Filter Metric Mismatch in Group-Relative RL With Shaped Rewards

A new arXiv paper examines how group-relative policy optimization methods such as GRPO drop rollout groups that show no contrast, through a dynamic sampling step, while real implementations let users configure the filter metric. The authors identify and measure a mismatch between that metric and the predicate used to decide which groups to discard, describing resulting "phantom advantages" when rewards are shaped. The work argues the filter metric choice is safety-critical rather than a minor implementation detail.

grpo/reinforcement-learning/safety/research

papersTODAY 04:00 UTC

GzDRL: Single-process deep reinforcement learning framework for Gazebo

Researchers introduce GzDRL, a reinforcement learning framework that runs Gazebo robotics simulations in a single process rather than relying on conventional middleware-based bridges. The authors say this design targets long-standing obstacles to reproducible and scalable robotics experimentation. The work is posted as an arXiv preprint (2609.13243).

robotics/reinforcement-learning/simulation/reproducibility

papersTODAY 04:00 UTC

arXiv paper proposes evolutionary framework for multi-agent Q-learning with mean-field feedback

A new arXiv preprint introduces an evolutionary computation approach to multi-agent reinforcement learning in networked populations. The framework combines individual adaptation, local interactions, and shifting environmental conditions through mean-field environmental feedback. The authors frame the work as a way to study how these coupled learning and environment dynamics interact.

multi-agent/reinforcement-learning/research/evolutionary-computation

papersTODAY 04:00 UTC

Hierarchical Deep Counterfactual Regret Minimization for Imperfect Information Games

A revised arXiv paper presents a hierarchical deep learning approach to counterfactual regret minimization, the algorithm family widely used to solve imperfect information games. The authors combine deep networks with a hierarchical structure intended to handle large game trees and skill-based strategy learning. It is a research contribution rather than a released product or model.

arxiv/game-theory/reinforcement-learning/cfr

papersTODAY 04:00 UTC

Admissable: Training RL Agents to Withstand Adversarial Missing Features

A new arXiv paper introduces Admissable, a training approach for reinforcement learning agents that must keep operating safely when an adversary deliberately removes or withholds input features. The work targets real-world deployments where sensor dropouts or tampered observations can degrade decision quality. It frames adversarial feature missingness as a distinct safety problem for RL rather than standard robustness to noise.

reinforcement-learning/adversarial-robustness/safety/arxiv

papersTODAY 04:00 UTC

arXiv Paper Reexamines Human Feedback for Robot Preference Learning

A new arXiv paper examines how robots typically build reward models of human preferences, a process that starts with collecting limited direct feedback such as positive or negative signals. The authors argue that the assumptions behind this standard three-step pipeline deserve reconsideration in the context of human-robot collaboration. The work is cross-listed in the cs.AI category.

human-robot-interaction/preference-learning/reinforcement-learning/research

papersTODAY 04:00 UTC

Privileged Observations Speed Up Physical-World Reinforcement Learning Policy Discovery

This paper investigates how extra, non-deployable information about a physical system's state influences how quickly and reliably a reinforcement learning agent learns effective control policies when trained on real hardware instead of simulation. The experiments use a cylinder on a tabletop water channel as the test setup. It is a revised preprint posted to arXiv's artificial intelligence and machine learning sections.

reinforcement-learning/robotics/arxiv/training

papersTODAY 04:00 UTC

Multi-Agent RL Approach to Factory Task Assignment and Navigation Tested on Real Robots

A new arXiv paper examines how multi-agent reinforcement learning can be applied to task assignment and navigation for robot fleets in industrial settings. The authors focus on the gap between simulation training and deployment on physical multi-robot systems, a step that remains difficult in practice. The work reports on transferring learned policies from simulated environments to real factory robots.

reinforcement-learning/robotics/multi-agent/sim-to-real

papersTODAY 04:00 UTC

Paper proposes safe meta-reinforcement learning via information space reachability

A new arXiv paper addresses safety in meta-reinforcement learning, where agents must adapt quickly to unfamiliar tasks. The authors propose using reachability analysis in an information space to keep adaptation within safe bounds. The work aims to make meta-RL more viable for real-world deployments that carry safety constraints.

safety/meta-learning/reinforcement-learning/research

papersTODAY 04:00 UTC

Variance-Penalized Actor-Critic Method Avoids a Second Critic for Risk-Sensitive RL

Researchers propose a nonparametric approach to variance-penalized reinforcement learning that trades expected return for policy stability without training a separate variance critic. The work frames risk-sensitive RL through statistical inference, aiming to cut the extra computation and complexity that online variance estimation usually requires. It is a new arXiv preprint in machine learning.

reinforcement-learning/arxiv/risk-sensitive/research

papersTODAY 04:00 UTC

Runtime-Incremental Transformer for Reinforcement-Learning-Based Adaptive Control

A new arXiv paper proposes a transformer-based meta-controller for adaptive control of robotic manipulators that must cope with friction memory they cannot directly observe. Existing attention-based controllers fix the number of attention heads before training and rely on expensive offline tuning; this work instead adjusts capacity incrementally at runtime. The authors position the method as a reinforcement-learning approach to adaptive control in a cross-listed machine learning submission.

reinforcement-learning/robotics/transformers/adaptive-control

papersTODAY 04:00 UTC

arXiv paper weighs planning against learning for multi-asset maintenance

A new arXiv preprint examines how industrial maintenance systems handle multiple interacting assets that share resources, where reliability and cost must be traded off together. The authors compare planning-based and reinforcement learning approaches, focusing on reliability and cost. It appears in the cs.AI and cs.LG listings.

reinforcement-learning/planning/maintenance/arxiv

papersTODAY 04:00 UTC

arXiv paper critiques on-policy self-distillation, proposes RL contrastive method

A revised arXiv paper examines on-policy self-distillation, a technique that gives reasoning models dense token-level feedback by matching their output distribution to one produced with extra context such as a verified solution. The authors argue this approach yields a flawed distribution, and they introduce RLCSD, which combines reinforcement learning with contrastive self-distillation on policy. The work is a preprint and has not been peer reviewed.

self-distillation/reinforcement-learning/reasoning/arxiv

papersTODAY 04:00 UTC

Researchers propose KL-projected natural policy gradient algorithms for Nash equilibrium learning in Markov potential games

A new arXiv paper studies decentralized learning of Nash equilibria in infinite-horizon discounted Markov games where agents only receive bandit feedback. The authors develop KL-projected natural policy gradient methods for both episodic and fully online asynchronous settings, aimed at Markov alpha-potential games. They also discuss applications to Markov congestion games.

reinforcement-learning/game-theory/arxiv/multi-agent

papersTODAY 04:00 UTC

arXiv Paper Proposes Specifying RL Reward Functions Without Environment Sampling

A new arXiv preprint describes a method for letting stakeholders define reward functions for reinforcement learning agents without needing to sample from the environment. The authors position the work as reducing the manual effort of reward design that preference-based approaches like online RLHF are meant to address. The same paper was listed in both the cs.AI and cs.LG announcement feeds.

reinforcement-learning/rlhf/reward-design/arxiv