Multilingual Fine-Tuning via Localized Gradient Conflict Resolution

arXiv:2606.05613 · cs.AI · Submitted 2026-06-04 · Read on arXiv

cs.AI

Submitted: 2026-06-04

Updated: 2026-09-27

Code: https://github.com/iNLP-Lab/BK-MOO

Terminology

• Fine-Tuning — 28× in this paper · explained in 5 episodes
• Multi-objective Optimization — 13× in this paper · explained in 4 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Aggregation — 7× in this paper · explained in 1 episode
• Alignment — 7× in this paper · explained in 1 episode
• Clustering — 7× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Communication Overhead — 4× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Catastrophic Forgetting — 2× in this paper · explained in 5 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Multiple Gradient Descent Algorithm — 2× in this paper
• Pruning — 2× in this paper · explained in 4 episodes
• Self-distillation — 2× in this paper · explained in 6 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Alignment Tax — 1× in this paper · explained in 4 episodes
• Cross-lingual Transfer — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection