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Research Editor selected

BLPO: Beyond-Local Policy Optimization for Long-Horizon LLM Agents

BLPO is a new reinforcement learning method for long-horizon LLM agents that estimates task-progress advantages from shared experience across tasks and updates, avoiding the need for a learned critic or extra rollouts. It summarizes trajectory prefixes into task-progress states and abstracts actions by function, using a global memory with balanced aggregation to control bias. Experiments on ALFWorld and WebShop with 1.5B and 7B policies show improved task success and faster learning under matched training budgets.

Why it matters: This paper introduces a concrete algorithmic improvement for training LLM agents on long-horizon tasks, with empirical gains on standard benchmarks, making it valuable for researchers and practitioners.

arxiv3d agoScore 85

LLM agentsreinforcement learninglong-horizon tasks

Research

MIRA: Reinforcing Multimodal Reasoning via Deceptive Contextual Augmentation

MIRA is a training framework that improves multimodal reasoning robustness by injecting deceptive visual contexts during training, creating 'hard traps' that stress-test models against misleading information. It also introduces a group-wise reflection-triggering mechanism for autonomous error detection and correction. Experiments show an average 7% improvement on in-domain benchmarks without human annotations.

arxiv3d agoScore 78

multimodal reasoningreinforcement learningrobustness

Research Editor selected

Axiomatic Framework for RL in Open Multi-Agent Systems

This paper proposes an axiomatic framework for reinforcement learning in open multi-agent systems, deriving value function learning from Shapley axioms. It introduces Shapley Machines and Banzhaf Machines, which improve learning efficiency and generalization over base algorithms like IPPO and POAM in NAHT benchmarks.

Why it matters: Provides a principled game-theoretic foundation and practical algorithmic templates for a challenging RL setting, with demonstrated improvements.

arxiv3d agoScore 85

Reinforcement LearningMulti-Agent SystemsCooperative Game Theory

Research Editor selected

WebHarbor: Docking Real Websites for Evolving GUI Agent Environments

WebHarbor is a suite of 153 executable, dockerized website environments that reconstruct real-world web services with deterministic control, covering 3,518 tasks including multi-step and post-authentication workflows. Built via human-agent collaboration, it provides stable evaluation and high-fidelity synthetic environments for RL training. A Qwen 4B-level GUI agent trained with RL in WebHarbor generalizes to unseen tasks and competes with leading open-source agents across four benchmarks.

Why it matters: Offers a concrete, novel infrastructure for GUI agent training and evaluation, with demonstrated generalization results, valuable for researchers and developers.

arxiv3d agoScore 85

GUI web agentsenvironment simulationreinforcement learning

Research Editor selected

Self-Improvement with Auxiliary Rewards: Learning from Verified Traces and Evaluating Transfer

This paper investigates using auxiliary trajectory preferences, specifically confidence-based (SC) and entropy-based (UID-Conj.) metrics, to enhance small language model self-improvement and transfer under limited supervision. Online experiments with Qwen3-1.7B on 30 AIME problems show UID-Conj. improves macro-average by 2.08 percentage points over unmodified TTRL, while offline weighted SFT yields a 1.93 point gain with a 95% CI of [0.44, 3.44]. The benefits are task-dependent, with SC rewarding instruction following and UID-Conj. producing stronger training outcomes in several settings.

Why it matters: Provides a concrete method for improving small LM self-improvement using trajectory preferences, with empirical gains on reasoning tasks and transfer benchmarks.

arxiv4d agoScore 85

reinforcement learninglanguage model trainingreward modeling

Research Editor selected

Subliminal Reinforcement Learning: traits travel through a judge's scores

A new study demonstrates that a student model can acquire a trait (e.g., preference for an animal) solely from a judge's scores during group relative policy optimization, even when the training data contains no semantic trace of that trait. The effect was confirmed across 17 out of 48 experimental cells, including those using only numbers. The authors identify a four-stage chain required for transfer and show that removing the reward difference between judges eliminates the effect. The findings have direct implications for AI safety, as undesirable dispositions could similarly propagate through judge scores.

Why it matters: Reveals a subtle but significant risk in reinforcement learning where traits can be transferred without explicit data, informing AI alignment and safety measures.

arxiv4d agoScore 88

reinforcement learningAI alignmentmodel behavior

Research

ART of PIV: Agentic Real-Time Particle Image Velocimetry for Active Flow Control

Researchers introduce ART of PIV, a learned agent that selects a particle image velocimetry pipeline configuration for each image pair under a specified speed budget. Across cross-correlation, classical optical-flow, and learning-based estimators, it reduced average end-point error by 12.2–22.4% relative to the best single estimator at matched speed, with gains extending to 1,000–4,000 image pairs per second. In a simulated velocity-regulation task, it lowered mean normalized closed-loop cost by 13.24–13.59 percentage points versus a tuned fixed estimator. The work also demonstrates tuning on a tabletop water channel and training a DreamerV3 reinforcement-learning controller in the physical world to track a prescribed flow field in a fluidic pinball wake.

arxiv4d agoScore 68

Particle image velocimetryActive flow controlReinforcement learning

Research

Pinching Antennas for ISAC: Waveguide Architectures, Solutions, and Future Directions

This arXiv paper reviews pinching antenna systems (PASS) for integrated sensing and communication (ISAC) in 6G wireless networks. It compares single-waveguide, multi-waveguide, and segmented-waveguide architectures, and presents a case study using segment-hysteresis reinforcement learning for joint optimization. The work highlights tradeoffs between waveguide loss and aperture gain, and outlines future research directions including digital twins and trustworthy control.

arxiv4d agoScore 72

integrated sensing and communication6G wireless systemsantenna array optimization

Research

From Representation to Readout: Mechanistic Analysis of Membership Signals in Language Models

This arXiv paper presents a mechanistic study of membership inference signals in large language models, introducing Decodable Information Score, Used Information Score, and Readout Accessibility Gap (DUG) to separate membership information in internal representations from information exposed through readout. Across supervised fine-tuning and reinforcement-learning-based membership inference methods, it reports membership-specific effects in both decodable information and readout under SFT, but primarily in readout under RL methods, with activation patching localizing effects to intermediate layers. The paper also examines reinforcement learning with verifiable rewards contamination and finds behavioral changes can emerge without a corresponding increase in likelihood-based membership inference signals.

arxiv4d agoScore 72

membership inferenceLLM interpretabilityreinforcement learning

Research Editor selected

KEEPER: Learning Control-Sufficient Textual Memory for Long-Horizon LLM Agents

The paper introduces KEEPER, a framework that analyzes auxiliary objectives for recursive textual memory in LLM agents, identifying self-consistency bias where incorrect claims persist in memory. It compares four supervision targets and finds reward-related prediction achieves the highest task performance before and after offline reinforcement learning. A claim-level analysis reveals error persistence despite corrective observations, motivating evaluation of error persistence and valid-information retention alongside task outcomes.

Why it matters: Provides a concrete, recent research contribution addressing a critical failure mode in long-horizon LLM agent memory, with practical implications for agent reliability.

arxiv4d agoScore 85

LLM agentsmemory managementreinforcement learning

Research

KARU: Reinforcement Unlearning of LLM Knowledge Associations

Researchers propose Knowledge Association (KA) as the target of LLM unlearning, defined as the conditional predictive distribution linking a query context to its target knowledge. They introduce Knowledge Association Reinforcement Unlearning (KARU), a reinforcement learning framework that combines token-level reward construction, distribution-level regularization, and proximity-aware retain sampling. Experiments across multiple LLMs and benchmarks report improved forgetting effectiveness while maintaining comparable retention and general utility.

arxiv4d agoScore 72

LLM unlearningKnowledge associationsReinforcement learning

Research

GRIT: Gradient Masking for Visually Misgrounded Tokens in Text-Rich Reasoning

The paper identifies Correct-but-Visually-Misgrounded Reasoning (CVMR) in vision-language models, where models can reach correct final answers despite misperceiving visual information during intermediate reasoning. It proposes GRIT, a training framework that incorporates visual grounding into reinforcement learning with verifiable rewards (RLVR) to mitigate CVMR. The authors also introduce GRIT-Bench for systematic evaluation across text length and reasoning depth. Experiments report improved reasoning accuracy and grounding fidelity over existing RLVR methods.

arxiv4d agoScore 76

vision-language modelsreinforcement learningvisual grounding

Research

TSBPO: Tree-Structured Sampling and Update for Training Interactive Language Agents

The paper introduces TSBPO, a tree-structured sampling-to-update training paradigm for interactive language agents trained from terminal outcomes. It combines a likelihood-fitted Root/Branch allocator that targets contrasting-outcome continuations, sibling-based trajectory advantages with observation-group step advantages, and complete-response token-loss aggregation. With a Qwen2.5-1.5B actor, the authors report 97.79% seen and 96.64% unseen stochastic success on ALFWorld, plus 81.07% per-episode success on WebShop over six stochastic episodes per test goal. An ablation and aggregation comparison are also reported.

arxiv4d agoScore 78

interactive language agentsreinforcement learningTSBPO

Research

LIT: Co-Evolving Video Director Agents Improve Production and Interaction

The paper introduces LIT, a framework for video director agents that co-evolves directing and user interaction capabilities. It uses a simulated user to probe weaknesses and a meta engineer to revise strategies, forming an evolutionary loop. On Test and OOD Test splits, LIT improves production and interaction scores by 19.1% and 32.0% relative to the initial pipeline.

arxiv4d agoScore 72

AI agent evolutionvideo generationhuman-AI interaction

Research

Geo-FSRL: Geometry-Optimized Few-Step Flow Reinforcement Learning

Geo-FSRL introduces a selection principle for stochastic policies in few-step flow models, minimizing conditional KL divergence for velocity-output updates. It derives a closed-form optimizer within a coefficient-preserving family of isotropic Gaussian transitions, improving training stability. The method achieves higher final reward scores than Flow-GRPO and Flow-CPS on image and video generation under 4-step settings.

arxiv4d agoScore 78

few-step flow modelsreinforcement learninggenerative AI

Research Editor selected

CRoS: A New Causality Reward for More Faithful Chain-of-Thought Reasoning

Researchers propose CRoS, a causality metric that perturbs the order of reasoning steps without altering content, avoiding confounding effects in existing perturbation-based metrics. On Reasoning Gym with Gemma-3-4B, CRoS improves out-of-domain accuracy, CoT quality, and faithfulness compared to existing rewards. It achieves 46.8% accuracy on unseen datasets, outperforming the strongest baseline by 3 percentage points.

Why it matters: This introduces a novel method to improve reasoning faithfulness in RL-trained models, with concrete benchmark gains, valuable for AI researchers and practitioners.

arxiv4d agoScore 82

Chain-of-Thought ReasoningReinforcement LearningCausality Metrics

Research Editor selected

MASCAL Trains AI Agents to Share Calibrated Confidence During Collaboration

The paper introduces MASCAL, a reinforcement-learning approach that trains collaborating AI agents to attach calibrated correctness probabilities to their answers. Confidence-aware weighting helps downstream agents learn to use these signals when deciding whether to trust or verify upstream results. Across three collaboration architectures covering Web retrieval and reasoning, the authors report improved accuracy and calibration, with final confidence scores supporting selective answering and targeted rechecking.

Why it matters: The research addresses unreliable handoffs between AI agents with an explicit confidence signal that can inform verification decisions.

arxivOct 30, 2026Score 84

MASCALMulti-agent collaborationConfidence calibration

Research

Value of Information Links Memory Retrieval and Credit Assignment in Reinforcement Learning

A new paper proposes using value of information, estimated from archived rollouts with a learned world model, to address both memory retrieval and credit assignment in reinforcement learning. The authors derive a lower bound on the value of an action that acquires an observation and use the same quantity to characterize optimal retrieval sets. A pilot retrieval system solves a memory task with reduced context, though the abstract does not report quantified latency or energy savings.

arxivOct 30, 2026Score 74

Reinforcement learningMemory retrievalValue of information