Papers with selective training
Selective Preference Optimization via Token-Level Reward Function Estimation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for maximizing preference optimization on all available tokens are noisy and inefficient. |
| Approach: | They propose a selective alignment strategy that centers on efficient key token selection without strong, fine-grained supervision signals. |
| Outcome: | The proposed strategy outperforms baseline methods on three benchmarks with up to 60% reduction in training hours. |
Tied-LoRA: Enhancing parameter efficiency of LoRA with Weight Tying (2024.naacl-long)
Copied to clipboard
| Challenge: | a new paradigm for low-rank Adaptation (LoRA) uses weight tying and selective training to improve parameter efficiency. |
| Approach: | They propose a paradigm that uses weight tying and selective training to enhance parameter efficiency of Low-rank Adaptation. |
| Outcome: | The proposed paradigm achieves comparable performance to LoRA with reduced model complexity . the proposed paradigm can be used for a variety of tasks and languages . |