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SpikeCredit: Temporal Credit Carrier for Reinforcement Learning with Sparse Rewards

arXiv:2609.35268v1 Announce Type: new Abstract: Reinforcement learning (RL) with sparse rewards is challenging because delayed outcomes provide little guidance about which intermediate computations ca

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StarOR: Synergizing Tree Search and Test-Time Reinforcement Learning for Optimization Modeling

DGX agent

arXiv:2606.15197v3 Announce Type: replace Abstract: Optimization modeling is inherently hierarchical, requiring a precise sequence of symbolic commitments. Traditional learning-based automated optimiz

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29 Sept 2026

Statistical Benefits of Fine-Tuning from Pretrained Initialization in Diagonal Linear Networks

arXiv:2609.34756v1 Announce Type: cross Abstract: Adapting pretrained models to downstream tasks with limited data has become a central paradigm in modern deep learning. Yet, despite its widespread pr

Sufficiency of Zeroth-Order Reward Shaping for Policy Gradient in Stabilization Control

arXiv:2609.34695v1 Announce Type: new Abstract: Reward shaping is fundamental to modern robotic control with deep reinforcement learning (RL), yet practitioners still rely heavily on heuristic princip

Surprising Success, Repeated Failure: Entropy-Guided Credit Assignment for Exploration in LLM Reasoning

arXiv:2609.33781v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) enhances reasoning in large language models (LLMs) through outcome-level feedback, yet recent appr

SymDrift: One-Shot Generative Modeling under Symmetries

arXiv:2605.06140v2 Announce Type: replace Abstract: Generative modeling of physical systems, such as molecules, requires learning distributions that are invariant under global symmetries, such as rota

SyncEdit: Rethinking Lip Synchronization as Editing with Audio-Driven Diffusion Models

arXiv:2603.09084v2 Announce Type: replace Abstract: Lip synchronization refers to the task of modifying facial lip movements such that they are temporally aligned with a given audio signal. It is a fu

TacGooseBumps (TacGB): Retrofitting Normal-Only Tactile Sensors with Shear Encoding for Learning Contact-Rich Manipulation

arXiv:2609.34006v1 Announce Type: new Abstract: Contact-rich policies often fail because distinct physical states look alike yet require different actions. Cameras may not reveal whether a connector i

Teach Yourself Where to Look: On-Policy Attention Self-Distillation for Reasoning

arXiv:2609.33200v1 Announce Type: new Abstract: On-policy self-distillation trains reasoning models on their own trajectories using dense token distribution guidance from a privileged teacher with acc

Teacher-Student Gaps Are Not Enough: Outcome-Guided On-Policy Distillation for Multi-Turn Autonomous Agents

arXiv:2609.35319v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own trajectories with dense teacher supervision. Recent work on OPD for multi-turn autonomous agent

Text-Vision Synergistic Token Caching: A Training-Free Framework for Efficient Vision-Language-Action Inference

arXiv:2609.34319v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models enable generalizable robotic control but remain computationally expensive. Token caching provides a training-free, p

The Devil is in the Spectrum Bias: Spectrum-Balanced Feature Matching for Robust Representation Distillation

arXiv:2609.34106v1 Announce Type: new Abstract: Large visual foundation models have demonstrated remarkable transferability across a wide range of downstream tasks. To deploy such models efficiently,

The Double-Edged Sword of Information: Revealed versus Hidden Lotteries in School Choice

arXiv:2609.34074v1 Announce Type: cross Abstract: In school choice, a lottery number is often used by the matching mechanism to break ties when there are more students who prefer the same school than

The Model Knows When to Stop: Training-Free Early Stopping for Long-Context Reading

arXiv:2609.34590v1 Announce Type: cross Abstract: Language models often process long inputs sequentially in chunks, but continuing to read after sufficient evidence has been acquired wastes computatio

The Platonic Universe: Do Foundation Models See the Same Sky?

arXiv:2509.19453v2 Announce Type: replace-cross Abstract: We investigate when foundation models converge towards shared representations, and how this convergence depends on model capacity, training re

The Statistical Benefits of Multiple Responses for Learning from Demonstrations

arXiv:2609.33291v1 Announce Type: cross Abstract: Many generative systems return multiple candidate responses and are evaluated according to the best one. Recent work shows that, when demonstrations a

Think Fast, Plan Selectively: Adaptive Deliberation for Efficient Data-Driven MPC

arXiv:2609.32591v1 Announce Type: cross Abstract: Data-driven model predictive control (MPC) combines learned world models with online trajectory optimization, achieving strong performance in continuo

TIDE: Teacher-Student Transition via Informative Distillation and Exploration for Agentic RL

arXiv:2609.35058v1 Announce Type: new Abstract: Effective multi-turn agents require interaction strategies that coordinate information gathering, actions, and feedback over long horizons. GRPO is a re

TimelyDAgger: Timing-Aware Expert Querying for VLA Policy Improvement

arXiv:2609.33157v1 Announce Type: new Abstract: DAgger improves robot policies by aggregating expert supervision from states visited during policy execution. Robot-gated DAgger automates expert querie

Timestep Weighting: A Hidden Key to Effective ELBO-Based Flow-Matching RL

arXiv:2609.32665v1 Announce Type: cross Abstract: ELBO-based reinforcement learning offers a sampler-agnostic approach to fine-tuning flow matching models with reward feedback. Timestep weighting in E

TokenCast: Forecasting Token Consumption During LLM Agent Execution

arXiv:2609.35760v1 Announce Type: new Abstract: When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnitude across runs. The agent chooses

Train Together or Merge Later? Unifying VLA Experts via a Shared Action Interface

arXiv:2609.33125v1 Announce Type: new Abstract: Co-training offers a straightforward way to build a multi-task vision-language-action (VLA) policy, but can fall short of the performance achieved by tr

Transfer Learning for Edge Classification on Dynamic Text-Attributed Graphs

arXiv:2609.32849v1 Announce Type: new Abstract: Learning transferable representations for dynamic text-attributed graphs (DyTAGs) requires models to capture underlying interaction dynamics that persis

Trump orders US government to call AI ‘Super Intelligence’

The US executive branch is no longer acknowledging the existence of 'artificial intelligence.' Going forward, official policy websites, policy documents, and press releases will refer only to 'Super I

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TSGate: Timestep-Aware Gated Attention for Diffusion Transformers

arXiv:2609.34539v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have emerged as the dominant architecture for high-fidelity image and video generation. Recent DiT systems increasingly us

TTRSD: Test-Time Reinforcement Learning with Self-Distillation for Vision-Language Models

arXiv:2609.33414v1 Announce Type: new Abstract: Test-time reinforcement learning enables vision-language models (VLMs) to adapt using unlabeled inputs. However, repeated sampling under fixed visual co

Twist, Don't Tilt: Trajectory-Exact Constrained Decoding for Masked Diffusion Models

arXiv:2609.35609v1 Announce Type: new Abstract: Constrained decoding for Masked Diffusion Language Models (MDLMs) aims to ensure that generated outputs satisfy a specified structure or syntax constrai

Two-Stage Learned Decomposition for Scalable Routing on Multigraphs

arXiv:2605.05389v2 Announce Type: replace Abstract: Most neural methods for Vehicle Routing Problems (VRPs) are limited to Euclidean settings or simple graphs. In this work, we instead consider multig

Two-Timescale Fine-tuning Provably Learns New Features for Two-Layer ReLU Networks

arXiv:2609.34667v1 Announce Type: cross Abstract: Fine-tuning pre-trained models on specialized tasks with scarce data is central to modern deep learning. Despite its empirical success, theoretical un

Unbiased Top-k Estimation for On-Policy Distillation

arXiv:2609.34447v1 Announce Type: cross Abstract: On-policy distillation (OPD) is becoming an important component of large language model (LLM) post-training for transferring the reasoning capability

Uncertainty Quantification of Next Generation Reservoir Computing with Applications to Memory-Driven Dynamical Systems

arXiv:2609.32169v1 Announce Type: cross Abstract: Nonlinear dynamical systems with memory arise across science and engineering, yet uncertainty quantification for efficient forecasting methods such as

Uncovering Ordinal-Matching Bias in Audio-Visual LLMs

arXiv:2609.34223v1 Announce Type: new Abstract: This work aims to improve how audio-visual large language models (AVLLMs) associate speech with the correct visible speaker in multi-speaker scenes. We

Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective

arXiv:2601.06597v3 Announce Type: replace Abstract: Can we design a model such that its stochastic training favours a desired class of solutions without enforcing an explicit penalty? Under suitable c

Understanding Generalization Requires Universal Induction

arXiv:2609.34458v1 Announce Type: cross Abstract: Classical statistical theory is insufficient to explain the successes of general-purpose AI models, because it depends on handcrafted inductive biases

UniCache: Task- and Type-Aware KV Cache Compression for Unified Multimodal Models

arXiv:2609.32831v1 Announce Type: new Abstract: Unified multimodal models combine understanding, generation, and editing within a single network, offering a promising foundation for versatile multimod

Unified Trajectory Matching Policy Optimization: Diverse T2I Generation and VLA Generalization

arXiv:2609.34688v1 Announce Type: new Abstract: Reward-maximizing reinforcement learning (RL) is widely used to post-train stochastic diffusion and flow policies for text-to-image (T2I) generation. Ho

UOPD: Uncertainty-Aware Intervention for On-Policy Distillation of Multi-Turn Agents

arXiv:2609.34036v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own rollouts using dense supervision from a teacher. In multi-turn environments, a mistake at a cri

Vanilla Policy Optimization Is Both Optimal and Differentially Private for Stochastic Contextual Bandits

arXiv:2609.33888v1 Announce Type: new Abstract: Can vanilla policy optimization explore enough to achieve near-optimal regret in stochastic contextual bandits? We show that standard exponential policy

Verifying the Linear Representation Hypothesis: How Interpretable Are Vision SAEs?

arXiv:2609.35020v1 Announce Type: new Abstract: Vision Sparse Autoencoders (SAEs) have become a popular tool in Mechanistic Interpretability due to their presumed ability to disentangle complex featur

WALA Learning Executable Latent Actions from Action-Labeled Demonstrations and Action-Free Videos

arXiv:2607.11397v2 Announce Type: replace Abstract: Human videos provide rich information about object manipulation at a scale difficult to reproduce with robots, but often lack action annotations tha

WAM-OPD: Sharpening World Action Models via On-Policy Distillation

arXiv:2609.34250v1 Announce Type: new Abstract: Pretrained world action models (WAMs) provide generalist capabilities across diverse robotic manipulation tasks, yet improving target-task performance t

Watch the Model Think: On-Policy Extraction of Activation Steering Vectors

arXiv:2602.14143v2 Announce Type: replace Abstract: When a model solves a problem on one attempt and fails it on the next, what separates the two is rarely the final answer token; it is the trajectory

Wavelet-Based Parity Detection Revisited: Representation Dependence, Generalization, and Mechanistic Analysis

arXiv:2511.00071v2 Announce Type: replace Abstract: Parity is exactly determined by the least significant bit (LSB), so machine learning is unnecessary for solving the task. This work instead uses par

Weighted Spline-Expanded Networks with Distributional Balancing for Continuous Treatment Effects

arXiv:2609.33740v1 Announce Type: cross Abstract: Estimating causal effects with continuous treatments in observational studies is challenging due to confounding, model misspecification, and high-dime

What Does a ProcGen Generalization Gap Measure? Action Rules, Convergence, and the Missing Random Floor

arXiv:2609.32532v1 Announce Type: new Abstract: A generalization gap in reinforcement learning (return on training levels minus return on held-out levels) is usually reported without a reference point

What Does the Rank Buy? A Spectral and Distributional Analysis of Low-Rank Adaptation

arXiv:2609.32002v1 Announce Type: cross Abstract: The rank r in LoRA is widely treated as a capacity control: a smaller rank is assumed to yield a simpler model that generalizes better. We show that,

What Paired Evaluations Reveal under Visual Perturbations

arXiv:2609.35583v1 Announce Type: new Abstract: Robustness evaluation must examine diverse visual perturbations, while benchmarks cover only some real-world conditions and physical testing is costly.

What Should the Reflector See? An Empirical Study of Evidence in Reflective Prompt Optimization

arXiv:2609.32452v1 Announce Type: cross Abstract: Reflective prompt optimization revises instructions using examples of a model's behavior, but which evidence the reflector should receive remains uncl