NeurIPS 2026 Papers

Reinforcement learning (RL) with verifiable rewards has proven effective at post-training LLMs for coding, yet deploying separate task-specific specialists incurs costs that scale with the number of tasks, motivating a unified multi-task RL (MTRL) approach. However, existing MTRL methods treat all coding tasks uniformly, relying on fixed data curricula under a shared optimization strategy, ultimately limiting the effectiveness of multi-task training. To address these limitations, we propose \textbf{{\tool}}, a multi-t\textbf{AS}k code reinforcement learning framework via u\textbf{T}ility-driven co\textbf{OR}dination. Centered on \textit{task utility}, a signal capturing each task's learning potential and cross-task synergy, {\tool} comprises two coupled modules: \textit{1) \moduleone} module hierarchically allocates training budget and prioritizes informative prompts, steering training toward the most valuable data; and \textit{2) \moduletwo} module dynamically scales per-task KL regularization, matching update constraints to each task's current training state. Experiments on two widely-used LLMs across four representative coding tasks demonstrate that {\tool} consistently improves a single model across all tasks, outperforming the best task-specific specialist by 9.0\%--9.5\% and surpassing the strongest MTRL baseline by 7.5\%--12.8\%.

Leveraging Graph Neural Networks (GNNs) as graph encoders and aligning the resulting representations with Large Language Models (LLMs) through alignment instruction tuning has become a mainstream paradigm for constructing Graph Language Models (GLMs), combining the generalization ability of LLMs with the structural modeling capacity of GNNs. However, existing GLMs that adopt GNNs as graph encoders largely overlook the problem of aligning GNN-encoded representations across domains and tasks with the LLM token space to obtain unified graph tokens, thereby limiting their ability to generalize across diverse graph data. To bridge this gap, we aim to incorporate a multi-domain, multi-task GNN encoder into GLMs and align its representations with LLMs to enable multi-domain, multi-task graph alignment instruction tuning. This alignment problem remains underexplored and poses two key challenges: 1) learning GNN-encoded representations that are simultaneously generalizable across domains and tasks and well aligned with textual semantics is difficult, due to substantial variations in graph structures, feature distributions, and supervision signals, together with the lack of textual-semantic alignment guidance in task-specific GNN training; 2) diverse graph data and task-specific instructions can exhibit different degrees of compatibility with the LLM token space during instruction tuning, leading to varying alignment difficulty and rendering a fixed alignment strategy suboptimal. To tackle these challenges, we propose UniGraphLM, a Unified Graph Language Model that incorporates a multi-domain, multi-task GNN encoder to learn generalizable graph representations aligned with textual semantics, and then adaptively aligns these representations with the LLM. Specifically, we first develop a graph-text pair pretraining strategy with a tailored GNN encoder, trained on large-scale graph-text data spanning multiple domains and tasks to obtain generalizable representations naturally aligned with textual semantics. We further design a curriculum alignment tuning strategy that adaptively adjusts the alignment process by accounting for varying alignment difficulty across diverse graph data. Extensive experiments demonstrate that UniGraphLM consistently outperforms state-of-the-art baselines across graph datasets from different domains and tasks.

Virtual sensors replace expensive physical sensors in critical applications through machine learning by predicting target signals from available measurements. Existing virtual sensor approaches require application-specific models with hand-selected inputs for each sensor, cannot leverage task synergies, and lack consistent benchmarks. While emerging time series foundation models offer general-purpose, pretrained solutions in other domains, they are computationally expensive and limited to predicting their input signals, making them incompatible with virtual sensors. We introduce the first multi-task model for virtual sensors addressing both limitations. Our unified model can simultaneously predict diverse virtual sensors exploiting synergies while maintaining computational efficiency. It learns relevant input signals for each virtual sensor, eliminating expert knowledge requirements while adding explainability. In our large-scale evaluation on three standard benchmarks and an application-specific dataset with over 18 billion samples, our architecture reduces computation time by up to 415x and memory requirements by 951x, while maintaining or even improving predictive quality compared to unified baselines. Compared to existing isolated models for a single virtual sensor, our unified approach generates superior predictions at similar inference speed while scaling gracefully to hundreds of virtual sensors with nearly constant parameter count, enabling practical deployment in large-scale sensor networks.

Class-incremental learning (class-IL) faces a structural obstacle: discriminative sequential training optimises only the diagonal blocks of a pairwise loss matrix, leaving the inter-task blocks---task confusion (TC)---unminimised. A recent Infeasibility Theorem makes this precise, proving that no discriminative class-IL learner can attain the joint-training optimum even when catastrophic forgetting (CF) is perfectly addressed. Yet the state of the art consists almost entirely of discriminative methods built on pretrained foundation models that approach joint-training performance. We explain this regime by showing that pretraining \emph{exponentially attenuates} the obstacle. For any pretrained backbone $\phi$, the TC gap of any CF-optimal prototype-based discriminative learner---a class that captures the test-time classifier of SLDA, RanPAC, FeCAM, and the prototype branches of EASE and InfLoRA---is bounded by $\exp(-\gamma_{\min}(\phi)/8)$, where $\gamma_{\min}(\phi)$ is the worst-case Mahalanobis margin between task centroids in feature space: a single, computable scalar that determines how much TC remains. Combined with a finite-sample convergence analysis under heavy-tailed gradient noise and a representation drift bound governing backbone fine-tuning, this yields a diagnostic decomposition of the class-IL excess risk into $E_{\mathrm{CF}} + E_{\mathrm{TC}}$, with disjoint direct dependencies on optimisation budget and backbone quality. Across 12 (backbone, dataset) configurations spanning ViT-IN21K, DINOv2, and CLIP plus four random-backbone anchor points, the pooled empirical slope of $\log(\mathrm{TC\ gap})$ versus $\widehat\gamma_{\min}$ on softmax cross-entropy heads is $-0.119$ (bootstrap 95\% CI $[-0.134, -0.105]$), consistent with the predicted $-1/8$ and excluding the conservative fall-back $-1/16$. The slope fit extends the rate beyond the formal LDA/QDA scope. $E_{\mathrm{CF}}$ scales as $K^{-(\alpha-1)/\alpha}$ as predicted, with the rate steepening by more than $3\times$ under gradient clipping; representation drift scales linearly in the backbone learning rate ($r = +0.998$). A theory-derived training procedure---whose schedule is determined by $\widehat\gamma_{\min}$, the tail index $\widehat\alpha$, and warm-up estimates of smoothness and curvature---is competitive with the strongest pretrained-CIL baselines across the four standard benchmarks.

Online Conformal Prediction (OCP) sequentially constructs prediction sets whose average empirical coverage converges to a target level over time, for arbitrary, potentially adversarial datasets. In the multi-level setting of OCP, prediction sets are produced at $d$ coverage levels at once. Prior work showed that online gradient descent with lazy isotonic projections produces sets properly nested across levels, but with a $\sqrt{d}$ dependence in the per-level coverage convergence rate. Here, we both generalize and improve prior work. First, we show that lazy isotonic projections can be safely used for the construction of multi-level online conformal prediction sets from \emph{any} online learning algorithm. Then, through a black-box reduction, we show that the linearized regret of the online algorithm itself automatically gives control over both per-level calibration and distributional consistency of multi-level predictions via Fenchel duality. Finally, by using the Weighted Interval Score, the standard scoring rule for assessing multi-level interval forecasts, we prove that these projections reduce both the size of the sets and their under-coverage. We show two instantiations of our theory, $p$-norm Online Mirror Descent and coordinate-wise Universal-Portfolio, both improving the dependence on the number of levels to $\sqrt{\log d}$ for the multi-OCP problem. Experiments on stock prices, electricity demand, and synthetic streams confirm consistent gains in coverage-width trade-offs over existing baselines.