arXiv Machine Learning
Aug 6
GFlowNet Training by Policy Gradients
arXiv:2408. 05885v3 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) have been shown effective to generate combinatorial objects with desired properties.
Reinforcement learning
Policy optimisation, reward modelling and RLHF — how models are trained by feedback rather than by labels.
arXiv Machine Learning
Aug 6
Communication-Enhanced Tutoring for Efficient Decentralized Multi-Agent Reinforcement Learning
arXiv:2508. 13661v4 Announce Type: replace Abstract: Centralized Training with Decentralized Execution (CTDE) is the dominant paradigm in multi-agent reinforcement learning (MARL), enabling agents to act independently at test time while leveraging additional information during training.
arXiv AI
Aug 6
Bi-Level Reinforcement Learning Pathway for Sim-to-Real Optimality
arXiv:2510. 17709v2 Announce Type: replace-cross Abstract: Training Reinforcement Learning (RL) policies using simulation models before deployment in real-world environments is a common strategy when real-world interaction is expensive.
arXiv AI
Aug 6
Can Post-Training Transform LLMs into Causal Reasoners?
arXiv:2602. 06337v2 Announce Type: replace-cross Abstract: Causal inference is essential for decision-making but remains challenging for non-experts.
arXiv AI
Aug 6
Perception Before Reasoning: Dynamic Latent Reasoning for Video Understanding and Question Answering
arXiv:2608. 04124v1 Announce Type: cross Abstract: Video question answering requires models to ground language queries in visual evidence and, when necessary, reason over that evidence across time.
arXiv AI
Aug 6
Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation
arXiv:2608. 04788v1 Announce Type: cross Abstract: Large language model agents are commonly trained through reinforcement learning with sparse trajectory-level rewards, which offer limited guidance on how strongly individual tokens should be updated.
arXiv Machine Learning
Aug 6
Optimizing What Policies Learn From: Recoverability-aware Rollout Intervention Learning
arXiv:2608. 05080v1 Announce Type: new Abstract: Critic-free group-based reinforcement learning has become a scalable approach for post-training large language models.
arXiv AI
Aug 6
EASy: Towards Efficient LLM-Based Agentic System
arXiv:2608. 04588v1 Announce Type: cross Abstract: Agentic systems have emerged as a promising paradigm for solving complex tasks by coordinating specialized LLM-based agents.
arXiv AI
Aug 6
Beyond the Dirac Delta: Mitigating Diversity Collapse in Reinforcement Fine-Tuning for Versatile Image Generation
arXiv:2601. 12401v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning large-scale generative models, such as diffusion and flow models, to align with complex human preferences and user-specified tasks.
arXiv Machine Learning
Aug 6
Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning
arXiv:2608. 05139v1 Announce Type: cross Abstract: Long-horizon reasoning in recent LLMs demands that the model switch between distinct skills inside a reasoning chain, such as first doing a math derivation, then using the result to plan a schedule.
arXiv AI
Aug 6
Teaching MLLMs to Say No: Generalized Referring Expression Comprehension via Refusal Calibrated GRPO
arXiv:2608. 04698v1 Announce Type: cross Abstract: We tackle the challenging yet underexplored task of Generalized Referring Expression Comprehension (GREC), which requires a model to localize the object described by a textual expression when it exists (positive sample) and to refuse output when it does not (negative sample).
arXiv AI
Aug 6
Reinforcement Learning and Consumption-Savings Behavior
arXiv:2510. 20748v2 Announce Type: replace-cross Abstract: This paper demonstrates how reinforcement learning can explain two puzzling empirical patterns in household consumption behavior during economic downturns.
arXiv AI
Aug 6
DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models
arXiv:2511. 15669v3 Announce Type: replace-cross Abstract: Does Chain-of-Thought (CoT) reasoning genuinely improve Vision Language Action (VLA) models, or does it merely add overhead?
arXiv Machine Learning
Aug 6
Distributional Active Inference
arXiv:2601. 20985v2 Announce Type: replace Abstract: Optimal control of complex environments with robotic systems faces two complementary and intertwined challenges: efficient organization of sensory state information and far-sighted action planning.
arXiv Machine Learning
Aug 6
State2State: Environment-Derived Mid-Training for LLM Agents
arXiv:2608. 04934v1 Announce Type: cross Abstract: Training LLM agents commonly relies on supervised fine-tuning from expert trajectories or online reinforcement learning over human-specified tasks with handcrafted verifiers.
arXiv Machine Learning
Aug 6
HCRide: Harmonizing Passenger Fairness and Driver Preference for Human-Centered Ride-Hailing
arXiv:2508. 04811v2 Announce Type: replace Abstract: Order dispatch systems play a vital role in ride-hailing services, which directly influence operator revenue, driver profit, and passenger experience.
arXiv AI
Aug 6
MediRec: Enhancing Chinese Medication Recommendation with Explainable Clinical Reasoning
arXiv:2510. 21084v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown strong potential for clinical decision support through their advanced language understanding and reasoning capabilities.
arXiv AI
Aug 6
FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks
arXiv:2608. 04073v1 Announce Type: cross Abstract: Personalized Federated Learning (PFL) has emerged as a promising solution for intrusion detection in heterogeneous IoT environments, as it can improve local adaptation under highly Non-Independent and Identically Distributed (non-IID) data distributions.
arXiv AI
Aug 6
Joint UAV Flight and Opportunistic Routing under Reinforcement Learning for Delay-Tolerant Networks
arXiv:2608. 04590v1 Announce Type: new Abstract: The growing deployment of delay-tolerant networks (DTNs) has made store-carry-forward (SCF) communication indispensable under sparse connectivity.
arXiv Machine Learning
Aug 6
Integrated Noise and Safety Management in UAM via A Unified Reinforcement Learning Framework
arXiv:2508. 16440v2 Announce Type: replace-cross Abstract: Urban Air Mobility (UAM) envisions the widespread use of small aerial vehicles to transform transportation in dense urban environments.