NeurIPS 2026 论文列表

Learning the natural parameters $z \in \mathbb{R}^n$ of discrete distributions $\mu_z$ from independent samples constrained to a subset $S \subseteq \\{0,1\\}^n$ is a foundational challenge in high-dimensional statistics. Existing methods for efficiently estimating truncated Boolean product distributions, notably the work of [Fotakis et al' COLT'20, Algorithmica '22], require either strong local connectivity assumptions on $S$ -- a property denoted *fatness* -- or stringent anti-concentration assumptions and necessitate the total mass of the truncation set to be a constant with respect to $n$. Moreover, the results in [Fotakis et al' COLT'20, Algorithmica '22] suffer from sample complexities that scale as $\Omega(2^n)$ if the mass of $S$ is exponentially small in $n$. In this work, we circumvent these limitations by analyzing the geometry of $S$ under the measure $\mu_z$. We refine the existing parameter estimation guarantees under the fatness assumption, improving the prior sample complexity to $\mathcal{O}( \log n / \epsilon^2)$ for $\ell_\infty$-recovery, matching the untruncated minimax rate. We further generalize fatness using the notion of influence utilized in the analysis of Boolean functions and provide sufficient conditions for efficient inference. Notably, unlike previous work, our method does not require sampling at arbitrary parameterizations of the model and instead relies on gradient descent. Lastly, we establish a theoretical lower bound demonstrating the sample complexity exhibits an intrinsic exponential dependence on the width of the model and the minimum distance between elements in the set.

Label distribution leakage in federated learning (FL) poses a critical privacy threat, exposing population-level patterns beyond individual data breaches. Building on known correlations between class sample frequencies and classifier weight magnitudes, we demonstrate how adversaries can exploit these relationships to infer label distributions from shared model parameters in FL. To characterize this threat, we propose WLIA (Weight Norm-based Label Distribution Inference Attack), which infers client label distributions by analyzing variations in classifier weight norms across communication rounds. WLIA operates on the classifier layer without requiring auxiliary data, synthetic samples, or meta-classifiers, enabling broader applicability across heterogeneous model architectures. To counter this threat, we introduce RNR (Randomized Weight Norm Regularization), a defense mechanism that disrupts frequency-weight correlations by randomly regularizing weight norms during local training. RNR adds only a penalty term to the cross-entropy loss, making it efficient and easy to integrate. Comprehensive experiments across six datasets demonstrate that WLIA achieves superior inference accuracy compared to existing attacks, while RNR effectively mitigates leakage with minimal impact on utility.

Large language model (LLM) inference serving is undergoing rapid growth and large-scale deployment, motivating us to rethink how the inference process itself can be leveraged to enable efficient task-specific LLM adaptation. In this paper, we propose a forward-only approach for efficient LLM adaptation with forward-only passes in LLM inference serving. We exploit the angle concentration of activations induced by each singular value decomposition (SVD) component to measure its contribution—_dispersed_ or _concentrated_—to the angle concentration of the hidden states. Based on this, our forward-only search then efficiently identifies the weight matrix with the highest dispersion that merits rank reduction. We then selectively remove the higher-order components and retain the lower-order components in SVD. Empirical results across diverse datasets demonstrate the competitive accuracy of our forward-only approach, while theoretical analysis shows lower peak memory and greater speedup than the gradient-based approach, and greater speedup than the exhaustive search approach. The extended experiments further present the robustness and generalization of our forward-only approach to various LLMs with up to 57B parameters. The code is included in the supplementary material.

Few-step image generation has seen rapid progress, with consistency and meanflow-based methods significantly reducing the number of sampling steps. Despite their low inference cost, these approaches often suffer from training instability and limited scalability. Sphere Encoder is a recent alternative that produces high-quality images in only a few steps; however, it requires repeated transitions between the pixel space and latent space during inference while jointly optimizing reconstruction and generation within a single architecture. This design leads to computational inefficiency and objective conflict between reconstruction and generation. To address these limitations, we decouple the framework into a fixed pretrained image encoder and a separate latent denoising model trained entirely in a spherical latent space. Our approach eliminates repeated pixel-space operations during training and inference, improving efficiency and allowing reconstruction and generation to specialize independently. On Animal-Faces, Oxford-Flowers and ImageNet-1K datasets, our method significantly outperforms Sphere Encoder in both generation quality and inference speed, while achieving competitive results against strong few-step and multi-step baselines.

Asynchronous reinforcement learning (RL) is an effective paradigm for improving the training efficiency of large language model (LLM) post-training by decoupling rollout generation from policy optimization. However, this decoupling introduces stale off-policy samples generated by earlier behavior policies, leading to distribution shift and unstable policy updates. Recent replay selection methods such as D-ARL mitigate this issue by selecting variance-aware samples, but require expensive current-policy evaluation over replay responses and mainly rely on variance reduction or current-policy matching as the selection criterion. To address these limitations, we propose GA-ARL, an efficient Gradient-Aware Asynchronous Reinforcement Learning framework for LLM post-training. GA-ARL derives an analytic target distribution from the KL-regularized RL objective and introduces an advantage-weighted variant to account for both policy preference and gradient strength. During training, GA-ARL maintains a replay buffer containing samples from recent behavior policies and selects low-discrepancy samples according to the advantage-weighted analytic target, avoiding additional current-policy evaluation during replay scoring. GA-ARL outperforms SOTA asynchronous methods across mathematical, logical, and code reasoning benchmarks, achieving the best average accuracy on both Qwen3-1.7B and Qwen3-4B, with improvements of up to 6.1%. Meanwhile, compared with the SOTA D-ARL, GA-ARL reduces wall-clock training time by 25.3% on average by avoiding additional current-policy evaluation during replay selection.

Structured sparsity extends classical sparse learning by encouraging sparsity at the level of predefined groups rather than individual parameters. This paradigm is widely adopted in modern machine learning and is typically enforced by sparsity-inducing norms. In real pipelines, the optimal group partition is often unknown a priori and does not remain static after initial training. Parameter groupings are often modified as domain knowledge or upstream representation evolves. Standard approaches require retraining the model from scratch under the new target partition, which discards the optimization effort already invested and becomes computationally prohibitive at scale. In this work, we focus on the group-wise $\ell_{p,1}$-norm, a flexible regularization family that encompasses classical group Lasso and other sparse variants. We develop a principled algorithmic framework that leverages the pre-trained optimum to fine-tune model weights toward a new desired group structure without full retraining. Technically, we decompose arbitrary group-wise transitions into a sequence of tractable primitives and derive the closed-form learning dynamics for each sub-action. Extensive experiments on real-world datasets demonstrate that our method achieves significant acceleration while provably recovering the correct optimal solution.

Exhaustively evaluating many large language models (LLMs) on a large suite of benchmarks is expensive. We cast benchmarking as finite-population inference and, under a fixed query budget, seek tight confidence intervals (CIs) for model accuracy with valid frequentist coverage. We propose *Factorized Active Querying* (FAQ), which (a) leverages historical information through a Bayesian factor model; (b) adaptively selects questions using a hybrid variance-reduction/active-learning sampling policy; and (c) maintains validity through *Pro-Active Inference*---a finite-population extension of active inference (Zrnic & Candès, 2024) that enables direct question selection while preserving coverage. With negligible overhead cost, FAQ delivers up to $5\times$ effective sample size gains over strong baselines on two benchmark suites, across varying historical-data missingness levels: this means that it matches the CI width of uniform sampling while using up to $5\times$ fewer queries. We release our source code and our curated datasets to support reproducible evaluation and future research.

Pre-trained diffusion and flow policies have emerged as powerful backbones for robotics, yet adapting them to novel tasks via reinforcement learning (RL) remains computationally unstable, sample inefficient and lacks theoretical guarantees. Prior approaches that fine-tune the latent noise space bypass costly backpropagation through time but struggle with "noise aliasing," where determining the optimal noise input to the pre-trained generative model requires repeatedly refitting separate Q-value networks. This paper resolves these challenges by introducing a novel theoretical framework demonstrating that imposing a linear structure on the environment induces a Latent Noise Linear Markov Decision Process (LNL-MDP). By treating the pre-trained model as a virtual dataset, our LNL-MDP fine tune framework falls under the FineTuneRL theory with near-optimal guarantee. To translate this idealized theory into a practical methodology, we derive the Bridge Equation which eliminates the noise aliasing problem. Leveraging this insight, we propose Latent Representation Bridging for Diffusion (LARBRID), an algorithm that efficiently learns the representation space from the LNL-MDP and optimal noises to steer the pre-trained model to act optimally in given tasks. We demonstrate that LARBRID is exceptionally sample efficient, yielding up to $2\text{-}3\times$ performance increases on challenging control problems across a diverse suite of robotic locomotion and manipulation tasks.

Construction-based neural routing solvers, typically composed of an encoder and a decoder, have emerged as a promising approach for solving vehicle routing problems. While recent studies suggest that shifting parameters from the encoder to the decoder enhances performance, most works restrict the decoder size to 1–3M parameters, leaving the effects of scaling largely unexplored. To address this gap, we conduct a systematic study comparing two distinct strategies: scaling depth versus scaling width. We synthesize these strategies to construct a suite of 12 model configurations, spanning a parameter range from 1M to $\sim$150M, and extensively evaluate their scaling behaviors across three critical dimensions: parameter efficiency, data efficiency, and compute efficiency. Our empirical results reveal that parameter count is insufficient to accurately predict the model performance, highlighting the critical and distinct roles of model depth (layer count) and width (embedding dimension). Crucially, we demonstrate that scaling depth yields superior performance gains to scaling width. Based on these findings, we provide and experimentally validate a set of design principles for the efficient allocation of parameters and compute resources to enhance the model performance.

Dataset distillation seeks to synthesize a highly compact surrogate dataset that achieves performance comparable to the original dataset on downstream tasks. For the scenario where pre-trained self-supervised models serve as priors, traditional $\textit{Linear Gradient Matching}$ optimizes synthetic images by encouraging them to mimic the gradient updates induced by real images on the linear probe or classifier. However, this batch-level formulation requires loading thousands of real images and applying multiple differentiable augmentations to synthetic images at each distillation step, leading to substantial computational and memory overheads. In this paper, we revisit the linear gradient and theoretically derive that it is essentially a $\textit{local}$ relative distribution directed from target class centers toward non-target class centers, which we term ``flow”. This property causes instability and suboptimality, often necessitating expensive multiple augmentations to compensate. To address this, we introduce $\textit{Statistical Flow Matching}$, a stable and efficient supervised learning framework that optimizes synthetic images by aligning $\textit{global}$ statistical flows in the original data. Our approach loads raw statistics only once and performs a single augmentation pass on the synthetic data, achieving performance comparable to or better than the state-of-the-art method with 10× less GPU memory usage and 4× faster distillation time. Moreover, increasing the number of augmentations for our method yields further performance gains while incurring lower additional cost.

The computational complexity of anonymous games has been a central theme in algorithmic game theory, with foundational contributions by Daskalakis and Papadimitriou [2015], Goldberg and Turchetta [2017], and Cheng et al. [2017] establishing that the landscape of equilibrium computation admits a PTAS. In this paper, we investigate the class of $n$-player anonymous games with linear utilities and two actions. While finding an equilibrium in anonymous games is PPAD-hard for arbitrary utility functions—even with a constant number of actions [Chen et al., 2015]—we show that this linear structure allows for significant algorithmic improvements. Exploiting the fact that payoffs depend solely on the first moment of the players’ strategy distribution, we provide the first algorithm for computing exact Nash equilibria that runs in time polynomial in the number of players $n$. We achieve this by establishing a novel combinatorial characterization of the Nash equilibria. Finally, we establish a novel connection between Nash equilibria in these games and non-convex non-concave min-max optimization. Despite the technical challenges posed by this structural property, we design a sequential best-response dynamic that provably converges to an $\epsilon$-Nash equilibrium in $\mathcal{O}(\frac{1}{\epsilon})$ steps.

Collaboratively fine-tuning (FT) large language models (LLMs) over heterogeneous mobile devices fosters immense potential applications of personalized intelligence. However, such a vision faces critical system challenges. Existing federated LLM fine-tuning approaches remain limited by prohibitive on-device resource overhead, straggler-prone synchronous aggregation, and the assumption of a unified pretrained backbone across heterogeneous devices. In this paper, we propose EC-MobiLLM, a novel design for efficient collaborative LLM fine-tuning across heterogeneous mobile devices and heterogeneous backbones. EC-MobiLLM adopts a server-assisted side-tuning paradigm to minimize on-device overhead and pioneers non-blocking asynchronous collaboration to accelerate training. Moreover, EC-MobiLLM introduces adaptive feature alignment to support collaboration across heterogeneous backbones, aligning with practical needs in real-world deployments. Extensive experimental results demonstrate that EC-MobiLLM can maintain robust fine-tuning performance while achieving extremely low on-device memory, with at least 95.2\% reduction in computation overhead, 93.2\% reduction in communication costs and $5.1\times$ faster convergence compared to existing methods, validating its efficacy for practical LLM adaptation over heterogeneous mobile devices.

Brain-to-speech decoding aims to restore communication by translating neural activity into speech-related representations. From a modeling perspective, this task can be formulated as a weakly supervised neural sequence decoding problem, where phoneme sequences must be recovered without frame-level annotations. Existing speech decoders commonly rely on recurrent sequence models to integrate temporal context, but their dense sequential computation can limit their suitability for resource-constrained portable or implantable neuroprosthetic systems. In this study, we propose a residual spiking neural network (SNN) for phoneme-level decoding of intracortical speech signals under constrained model capacity and weak sequence-level supervision. Our approach incorporates biologically inspired fixed connection-specific delays to integrate temporal context, providing each postsynaptic unit with access to recent presynaptic spike activity without recurrent state or dense temporal convolutions. Because each directed connection is assigned a single fixed delay, the temporal field is expanded without adding trainable delay parameters. Across 500k, 2M, and 5M core-parameter budgets, fixed delays consistently improve phoneme error rate (PER), with gains saturating once the delay range covers the task-relevant temporal horizon. Boundary-aligned CTC analysis further suggests that delayed temporal context reduces local uncertainty around phoneme transitions, indicating that fixed delays support weakly supervised phoneme alignment rather than simply increasing model capacity. Compared with recurrent, convolutional, and feedforward baselines under matched core-parameter budgets, the proposed SNN achieves the best PER at 500k and 2M parameters and the second-best PER at 5M parameters, while reducing estimated operation-level energy by approximately 2-4$\times$ relative to the strongest non-spiking baselines. These results suggest that fixed-delay SNNs provide an efficient temporal modeling mechanism for brain-to-speech decoding, potentially enabling lightweight decoding for on-device speech neuroprostheses.

Efficient benchmarking techniques aim to lower the computational cost of evaluating LLMs by predicting full benchmark scores using only a subset of a benchmark's questions. By reframing this problem as an instance of multiple regression with feature selection, we find that existing efficient benchmarking methods can be greatly improved by simply using kernel ridge regression at the prediction stage. Additionally, using an information-theoretic feature-selection algorithm called minimum redundancy maximum relevance (mRMR), we can further improve upon these methods by selecting question subsets that will be maximally useful for prediction. Except in very data-poor settings, these approaches consistently achieve smaller prediction errors (in both MAE and RMSE), and greater ranking correlation between predicted and true scores (in both Spearman $\rho$ and Kendall $\tau$) across a range of benchmarks using both binary and continuous metrics. Furthermore, mRMR subsampling is much faster than competitor methods (which often involve fitting probabilistic models or running clustering algorithms), and is more likely to select the same questions under different random seeds or training data splits.

The distributed setting for Saddle Problems (SPs) has recently emerged as a framework for modern applications in machine learning and multiagent systems. Despite its relevance, the theoretical foundations of this setting have not yet been thoroughly established. In this paper, we advance this research direction by formalizing the distributed setup for SPs and providing rigorous definitions of communication and oracle costs. Further, we prove lower bounds for any distributed gradient-span algorithm, which reveals the gap from existing methods and this theoretical limit. To this end, we provide a Decoupled Method built upon a novel multi-stage reduction that reduces the SP into a sequence of decoupled minimization tasks of residual norms. Our algorithm matches the communication lower bound, thus setting the communication complexity within the gradient-span algorithms. Moreover, it yields the first strict improvement over the long-standing oracle cost of the Extragradient method for general SPs. Finally, we study the extension of distributed SP into Variational Inequality Problem (VIP), which generalizes two-player zero-sum games to multiplayer general-sum games. We show that our Decoupled Method achieves a new state-of-the-art communication complexity for this broader class.

We consider the problem of maintaining a minimum-cost bipartite matching between two point sets $A$ and $B$ in a metric space $(\mathbb{X},\mathsf{d})$ under insertions and deletions of input points. The Wasserstein-$p$ ($W_p$) cost of a matching $M$ is defined as $(\sum_{(a,b)\in M}\mathsf{d}(a,b)^p)^{1/p}$ and an $\alpha$-approximate matching is one whose total cost is at most $\alpha$ times the cost of a minimum-cost matching. We obtain two main results. If $A$ and $B$ are points in $\mathbb{R}^d$ and the distance between two points is measured under the $\ell_p$-metric, an $O(d\epsilon^{-3/2})$-approximate $W_1$-matching between $A$ and $B$ can be maintained with an amortized $O\big(n^\epsilon d\epsilon^{-3/2}\varphi(n,1/\sqrt{\epsilon})\big)$ update time, for any parameter $\epsilon\in(0,1]$, and where $\varphi(n,1/\sqrt{\epsilon})$ denotes the update time of a $\epsilon^{-1/2}$-approximate nearest neighbor data structure. For the $\ell_2$ norm this results in an update time of $O\big((d+ \epsilon^{-3/2}n^\epsilon)\log n\big)$. The current state-of-the-art for high-dimensional settings only focus on the $\ell_2$-metric and maintain an $O(\log n)$-approximate matching. Our result not only extends to any $\ell_p$ norm, but it also improves the approximation factor for $d=o(\log n)$ under the $\ell_2$-metric. Next, we consider the problem of maintaining a $W_p$-matching when $A$ and $B$ are point sets in an arbitrary finite metric space. We show that for any $k,p\geq 1$ and constant $\epsilon>0$, a $2k(1+\epsilon)$-approximate matching can be maintained with an amortized update time of $O(kn^{1+1/k}\epsilon^{-1}\log \Delta)$, where $\Delta$ is the spread of $A\cup B$. Prior work on finite metric spaces gave an insertion-only data structure for $W_1$-matching. In contrast, we develop a fully dynamic approach that supports both insertions and deletions and work for all $W_p$-matchings. Together, our results extend dynamic Wasserstein matching beyond fixed-dimensional $W_1$ settings: they handle high-dimensional geometric instances, extend prior insertion-only $1$-Wasserstein matching guarantees to the fully dynamic setting, and support $p$-Wasserstein costs for every integer $p\ge 1$ in arbitrary metric spaces.

Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximations that can be misspecified or biased, while direct policy-learning methods map from historical observations and fail to exploit available model representations, making learning harder. We introduce policy learning with belief representations (POLAR), based on the insight that optimal data acquisition depends on the observation history only through a sufficient belief state. Specifically, POLAR decouples representation learning from policy learning by leveraging pretrained predictive foundation models as belief-state encoders, training a policy head on top of their representations. This yields a simple, unified amortised policy learning framework for Bayesian experimental design, Bayesian optimisation, and active learning, differing only in the task-specific utility used to train the policy. Empirically, we find that POLAR outperforms state-of-the-art amortised methods across diverse tasks while requiring far fewer training samples, demonstrating a significant step in the scalability and efficiency of amortised data acquisition.

While animals and people tend to learn a task more quickly and reliably when they first train on simpler versions of it, curriculum learning's effectiveness in artificial settings varies, and appears substantially greater in reinforcement learning than in supervised learning. In this work, we construct and analyze a minimal model of policy learning to better understand why. We consider an open-loop, episodic target interception task whose parameters---especially how close the agent must be to the target to receive an informative reward signal---can be chosen so that the agent either receives informative rewards frequently ('dense' rewards), or rarely ('sparse' rewards). We mathematically and numerically analyze the dynamics of REINFORCE-based policy learning in both cases. In the dense reward case, curricula have only marginal benefits; in the sparse case, curricula can be required for learning to occur at all. We find that this is especially true when the agent has the conflicting goals of minimizing effort and accumulating high reward, since this can produce distinct reward landscape optima which compete with one another. Our characterization of useful curricula in this setting indicates that they address two issues: they (i) make rewards less sparse to speed up learning, and (ii) shape the reward landscape to steer agents away from bad local optima.

Knowledge conflict detection is a fundamental challenge for large language model (LLM) systems that rely on external knowledge. Existing approaches address this problem either with end-to-end supervised models or with multi-stage pipelines dependent on fixed heuristics. However, supervised models often generalize poorly to diverse conflict patterns, whereas heuristic pipelines rely on fixed rules and introduce considerable computational overhead. To overcome these limitations, we propose $\textbf{G}$rouping and $\textbf{C}$onflict $\textbf{D}$etection ($\mathsf{GCD}$), a two-stage agentic framework for knowledge conflict detection that aligns the detection process with the inherent sparsity of knowledge conflicts, thereby reducing the decision space and enabling fine-grained conflict detection. $\mathsf{GCD}$ decomposes knowledge conflict detection into two subtasks and employs two cooperative agents to address them: a Knowledge Grouping Agent (G-Agent), which creates conflict-preserving groups of textual units with potential conflicts, and an Intra-group Conflict Detection Agent (D-Agent), which detects conflicts within each group. The G-Agent's grouping policy is not fixed in advance but is jointly trained with D-Agent via a shared global reward, enabling grouping to be explicitly optimized for accurate detection rather than relying on fixed heuristics. Extensive experiments on five benchmarks demonstrate that $\mathsf{GCD}$ consistently outperforms SOTA methods, while reducing inference latency and token consumption.