multi-task-learning

This paper presents a strategy to adapt an open-source spoken language model to the Singaporean Home Team context using LoRA fine-tuning, a surrogate text-QA dataset, and a multi-task objective, achieving competitive performance across five speech tasks in Singapore's four official languages.

This paper presents AffectFlow-DINO, a multi-task learning system for the 11th ABAW challenge that uses a conditional rectified-flow head to model uncertainty in in-the-wild facial behavior estimation, achieving substantial improvements over the baseline.

GenCeption repurposes pre-trained video generative models into a single unified feed-forward vision model that achieves state-of-the-art performance across multiple tasks with exceptional data efficiency, marking a shift toward general-purpose visual intelligence.

This thread discusses modifications to GRPO for agentic RL, focusing on different levels of advantage normalization (prompt-level, task-level, environment-level) to handle higher reward variance in multi-task, multi-turn environments.

The paper introduces DMVM, a decentralized multi-task dataset valuation framework that leverages task arithmetic and model merging to estimate dataset contributions without retraining or data sharing, enabling scalable and privacy-preserving valuation for data marketplaces.

Andrej Karpathy gives a 15-minute talk on the challenges of multi-task learning, covering architecture tradeoffs, loss balancing, data engines, and team workflow, highlighting often-overlooked team coordination issues.

NeuroBridge is a clinically guided multi-task MRI framework for diagnosing Alzheimer's disease and mild cognitive impairment, achieving high accuracy and cross-cohort generalization.

Proposes FRAME, a mixture-of-experts adapter that uses learnable fractional-Fourier orders to interpolate between spatial and spectral domains, improving parameter-efficient fine-tuning performance on LLMs across multiple benchmarks.

HierBias introduces a hierarchical context-conditioned model for media bias detection that leverages document context to improve sentence-level classification, achieving state-of-the-art F1 and MCC on the BABE and BASIL datasets.

This paper introduces LDM-v0, a large decision model trained offline on trajectories from thousands of diverse reinforcement learning environments, demonstrating that a single transformer policy can match the performance of task-specific policies across robotics, autonomous driving, inventory management, cybersecurity, trading, and video games.

SURGeLLM introduces a unified transformer framework with surgical feature gates, task-conditioned prefix tokens, and instance-weighted normalization to address mismatched inductive biases, class imbalance, and lexical knowledge injection in multi-task learning, achieving significant gains across four diverse NLP tasks.

This paper identifies a capacity-induced failure mode in physics-informed neural networks (PINNs) where overparameterized networks develop functional modularity that hinders convergence, and proposes Modular-Sparsity Synchronization (ModSync), a framework that penalizes task-exclusive connections to maintain cross-objective interaction and achieve state-of-the-art accuracy.

The paper identifies 'Load-Bearing Wall' dimensions in pre-trained models that retain task-specific knowledge not fully captured by task vectors in model merging, and proposes PACT (PreserveAnchoredCores) to preserve these cores, achieving state-of-the-art performance across benchmarks.

OneRank proposes a Transformer-native multi-task ranking framework that integrates feature encoding and prediction to reduce inter-task interference and improve ranking performance in recommender systems.

This paper proposes a probabilistic contrastive pretraining framework for molecular graph transformers to improve multi-task ADME property prediction in drug discovery, achieving significant gains on three benchmarks.

This paper introduces World-Language-Action (WLA) models, embodied foundation models that jointly predict textual subtasks, subgoal images, and robot actions from text, images, and robot states, achieving state-of-the-art multi-task and long-horizon learning in simulated and real-world environments.

Proposes MechRL, a reinforcement learning approach to automate circuit discovery in transformer language models. A PPO agent trained on multiple tasks discovers attention head circuits that match known canonical circuits and generalizes to a held-out task.

This paper develops a PAC-Bayesian framework for physics-informed machine learning, providing high-probability generalization guarantees for unbounded losses. It proposes a multi-task perspective that jointly handles data fidelity, PDE residuals, and boundary conditions, and introduces a self-bounding learning algorithm.

This paper proposes a task-routed mixture-of-experts model with cognitive appraisal theory for implicit sentiment analysis, introducing auxiliary tasks to improve reasoning about sentiment from context and outperforming existing approaches.

This paper introduces an interference-aware framework for multi-task machine unlearning, addressing task-level and instance-level interference through task-aware gradient projection and instance-level gradient orthogonalization, achieving effective unlearning on multi-task computer vision benchmarks.