arXiv AI
Aug 11
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning
arXiv:2608. 08158v1 Announce Type: new Abstract: Sparse, delayed, and weakly informative rewards remain central obstacles to efficient reinforcement learning.
Reinforcement learning
Policy optimisation, reward modelling and RLHF — how models are trained by feedback rather than by labels.
arXiv AI
Aug 11
Learning an Interior Layout Policy in a Domain Specific Language Action Space
arXiv:2608. 07547v1 Announce Type: cross Abstract: Indoor scene layout generation is a challenging task in interior design.
arXiv AI
Aug 11
Beyond Pixels: Benchmarking and Reward-Based Assessing Framework for Visual Spatial Aesthetics
arXiv:2512. 05098v2 Announce Type: replace-cross Abstract: In recent years, Image Quality Assessment (IQA) for AI-generated images (AIGI) has advanced rapidly; however, existing methods primarily target portraits and artistic images, lacking a systematic evaluation of interior scenes.
arXiv AI
Aug 11
Evidence-RL: Towards Evidence-intensive Visual Reasoning
arXiv:2608. 08021v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) should answer from concrete image evidence rather than language priors, dataset shortcuts, or irrelevant visual context.
arXiv AI
Aug 11
Argus: A General-Purpose Agentic Reasoning Runtime for Long-Horizon Tasks
arXiv:2608. 05144v2 Announce Type: replace Abstract: Long-horizon reasoning requires an agentic runtime that can persist when evidence supports its current approach and pivot when measurements reveal failure, hidden constraints, or a misspecified objective.
arXiv AI
Aug 11
When LLM Agents Negotiate: Private Information and Dynamic Bargaining in Supply Chains
arXiv:2608. 07538v1 Announce Type: new Abstract: As LLM agents move from decision support to autonomous procurement, firms need to know whether delegated negotiators create value, divide it predictably, and avoid money-losing contracts.
arXiv AI
Aug 11
SMAC: Score-Matched Actor-Critics for Robust Offline-to-Online Transfer
arXiv:2602. 17632v3 Announce Type: replace-cross Abstract: Modern offline Reinforcement Learning (RL) methods find performant actor-critics, however, fine-tuning these actor-critics online with value-based RL algorithms typically causes immediate drops in performance.
arXiv AI
Aug 11
SCOUT: Self-Checking and Recovery-Aware Tool-Thought Agents for Ultra-Long Egocentric Video Reasoning
arXiv:2608. 07959v1 Announce Type: new Abstract: Ultra-long egocentric video understanding requires reasoning over temporally sparse evidence distributed across hours or days, challenging current multimodal models with limited context and the grounding of key video segments.
arXiv AI
Aug 11
Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models
arXiv:2608. 08010v1 Announce Type: cross Abstract: Time series forecasting (TSF) plays an important role in a wide range of real-world applications.
arXiv Machine Learning
Aug 11
Learning from Environmental Feedback: Credit Assignment across Multiple Timescales for Agentic Reinforcement Learning
arXiv:2608. 08255v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) often suffers from delayed and sparse rewards in real-world environments.
arXiv Machine Learning
Aug 11
Catastrophic Forgetting in Continual Reinforcement Learning
arXiv:2608. 08673v1 Announce Type: new Abstract: This work explores the relationship between task similarity and catastrophic forgetting in reinforcement learning.
arXiv Machine Learning
Aug 11
Tracking the Best Strategy in an Extensive-Form Game
arXiv:2608. 09501v1 Announce Type: new Abstract: We consider the extensive-form bandit problem where on each trial the learner plays an extensive-form game against an oblivious adversary.
arXiv Machine Learning
Aug 11
Regret of exploratory policy improvement and $q$-learning
arXiv:2411. 01302v2 Announce Type: replace Abstract: We study the convergence of $q$-learning and related algorithms introduced by Jia and Zhou (J.
arXiv AI
Aug 11
Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning
arXiv:2608. 09507v1 Announce Type: cross Abstract: Natural language user preferences provide an interpretable interface for LLM personalization.
arXiv AI
Aug 11
StructReward: Efficient Structured Process Rewards for Self-Correcting Multimodal Reasoning
arXiv:2608. 08326v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective approach for improving multimodal reasoning.
arXiv AI
Aug 11
IndexTTS 2.5 Technical Report
arXiv:2601. 03888v4 Announce Type: replace-cross Abstract: In prior work, we introduced IndexTTS 2, a zero-shot neural text-to-speech foundation model comprising two core components: a transformer-based Text-to-Semantic (T2S) module and a non-autoregressive Semantic-to-Mel (S2M) module, which together enable faithful emotion replication and establish the first autoregressive duration-controllable generative paradigm.
arXiv AI
Aug 11
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning
arXiv:2608. 09138v1 Announce Type: cross Abstract: While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often hindered by suboptimal execution speeds.
arXiv Machine Learning
Aug 11
Process Supervision of Confidence Margin for Calibrated LLM Reasoning
arXiv:2604. 23333v2 Announce Type: replace Abstract: Scaling test-time computation with reinforcement learning (RL) has emerged as a reliable path to improve large language models (LLM) reasoning ability.
arXiv AI
Aug 11
SuperCoder: Assembly Program Superoptimization with Large Language Models
arXiv:2505. 11480v4 Announce Type: replace-cross Abstract: Superoptimization is the task of transforming a program into a faster one, and ideally the very fastest possible one, while preserving its input-output behavior.
arXiv AI
Aug 11
Reading is not Reasoning: Bridging the Agentic Policy Gap in Vision-Text Compression
arXiv:2608. 08960v1 Announce Type: new Abstract: Multi-step language-model agents repeatedly process growing interaction histories, leading to substantial context costs.