NeurIPS 2024 Past Other
NeurIPS 2024 Workshop on Fine-Tuning in Modern Machine Learning: Principles and Scalability
FITML 2024
- Submission deadline
- Oct 1, 2024, 23:59 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (88)
Fetched from OpenReview (v2) on 2026-06-10.
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A Layer Selection Approach to Test Time Adaptation
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A Tensor-based Convolutional Neural Network for Small Dataset Classification
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Accelerating Direct Preference Optimization with Prefix Sharing
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ActNAS : Generating Efficient YOLO Models using Activation NAS
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Adapting Language Models via Token Translation
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Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models
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An empirical study of CLIP fine-tuning with similarity clusters
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Analysing Softmax Entropy Minimization for Adaptating Multitask Models at Test-time
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Balancing Cost and Effectiveness of Synthetic Data Generation Strategies for LLMs
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Best Unpacking DPO and PPO: Disentangling Practices for Learning from Preference Feedback
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Characterizing the Training Dynamics of Private Fine-tuning with Langevin diffusion
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COMAL: A Convergent Meta-Algorithm for Aligning LLMs with General Preferences
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Comparing Bad Apples to Good Oranges: Aligning Large Language Models via Joint Preference Optimization
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CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation
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Deep Reinforcement Learning Without Experience Replay, Target Networks, or Batch Updates
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Discrepancy-Guided Parameter Suppression for Robust Fine-tuning
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DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agent
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E-Tamba: Efficient Transformer-Mamba Layer Transplantation
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Early Exiting in Deep Neural Networks via Dirichlet-based Uncertainty Quantification
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Effective Text-to-Image Alignment with Quality Aware Pair Ranking
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Efficient Fine-Tuning of Behavior Cloned Policies with Reinforcement Learning from Limited Demonstrations
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Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs
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Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation
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Ensembling Finetuned Language Models for Text Classification
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Entropic Distribution Matching for Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity
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Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies in Medical Question Answering
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Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model
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Faster, More Efficient RLHF through Off-Policy Asynchronous Learning
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FedEx-LoRA: Exact Aggregation for Federated Parameter-Efficient Fine-Tuning of Foundation Models
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Fine tuning language models to align fidelity and efficiency of generative retrieval in multi-turn dialogues
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Fine-tuning Vision Classifiers On A Budget
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Fitness Aware Human Motion Generation with Fine-Tuning
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Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape
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Flexora: Flexible Low-Rank Adaptation for Large Language Models
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FourierKAN outperforms MLP on Text Classification Head Fine-tuning
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FRACTAL: Fine-Grained Scoring from Aggregate Text Labels
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GaLore-mini: Low Rank Gradient Learning with Fewer Learning Rates
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Generalizing Alignment Paradigm of Text-to-Image Generation with Preferences through $f$-divergence Minimization
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Hierarchical Unlearning Framework for Multi-Class Classification
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HyperDPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework
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ImageNet-RIB Benchmark: Large Pre-Training Datasets Don't Guarantee Robustness after Fine-Tuning
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Improving Fine-Tuning with Latent Cluster Correction
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Improving LLM Generation with Inverse and Forward Alignment: Reward Modeling, Prompting, Fine-Tuning, and Inference-Time Optimization
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Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples
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Inducing Semi-Structured Sparsity by Masking for Efficient Model Inference in Convolutional Networks
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Instant Transformer Adaption via HyperLoRA
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Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning
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Investigating the Role of Fine-Tuning in Addressing the Gap Between Synthetic and Real Data in Generative Foundation Models
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Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment
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Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational Objective
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LLM Alignment Through Successive Policy Re-weighting (SPR)
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Mastering Task Arithmetic: $\tau$Jp as a Key Indicator for Weight Disentanglement
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Memory retaining finetuning via distillation
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Model Soup for Better RLHF: Weight Space Averaging to Improve Alignment in LLMs
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MPLoRA: Orthogonal Multi-Path Low-Rank Adaptation for Parameter Efficient Fine-Tuning
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Navigating Parameter Space with Geodesic Interpolation: A New Approach to Efficient Fine-Tuning
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Noise Stability Optimization for Finding Flat Minima: A Hessian-based Regularization Approach
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On Efficient Distillation from LLMs to SLMs
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On the Transferability of Parameter-Efficient Continual Learning for Vision Transformers
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One Initialization to Rule them All: Fine-tuning via Explained Variance Adaptation
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Online Fine-Tuning with Uncertainty Quantification for Offline Pre-Trained Agents
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Optimizing Small Language Models for In-Vehicle Function-Calling
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PAL: Pluralistic Alignment Framework for Learning from Heterogeneous Preferences
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Parameter-Efficient Fine-Tuning of State Space Models
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Parasite Networks: Transfer Learning in Resource-Constrained Domains
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REACT: Residual-Adaptive Contextual Tuning for Fast Model Adaptation in Cybersecurity
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RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates
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Scalability of memorization-based machine unlearning
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Self-Stitching: Widely Applicable and Efficient Transfer Learning Using Stitching Layer
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Semi-Supervised Fine-Tuning of Vision Foundation Models with Content-Style Decomposition
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Sharp Analysis for KL-Regularized Contextual Bandits and RLHF
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Simultaneous Weight and Architecture Optimization for Neural Networks
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Skip Transformers: Efficient Inference through Skip-Routing
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SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors
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Teaching LLMs How To Learn with Contextual Fine-Tuning
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Token Pruning using a Lightweight Background Aware Vision Transformer
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TOU: Truncated-factorized reduction for an efficient-parameter model fine-tuning
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Towards Long-Context Time Series Foundation Models With A Handful Of Additional Parameters
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Towards Natural Machine Unlearning
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TreeTop: Topology-Aware Fine-Tuning for LLM Conversation Tree Understanding
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Uncertainty-Penalized Direct Preference Optimization
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Understanding Visual Concepts Across Models
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Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization
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UnoLoRA: Single Low-Rank Adaptation for Efficient Multitask Fine-tuning
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Variational Best-of-N Alignment
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Variational Low-Rank Adaptation Using IVON
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What Causes a Disparate Impact in a Quantized Model?
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XoRA: Expander Adapted LoRA Finetuning