Goal-Conditioned Supervised Learning for LLM Fine-Tuning
A recent publication on arXiv introduces goal-conditioned supervised learning (GCSL) as a framework for offline fine-tuning of large language models. This technique utilizes feedback signals as clear objectives and relies solely on supervised learning, eliminating the expenses and intricacies associated with online reinforcement learning alignment or the necessity for paired preference data in DPO. GCSL seeks to address the shortcomings of current offline strategies such as SFT, which reduces graded feedback to binary supervision, and DPO, which demands costly preference data. The paper can be found under the identifier arXiv:2605.16345v1.
Key facts
- Paper proposes goal-conditioned supervised learning (GCSL) for LLM fine-tuning.
- GCSL is an offline framework that treats feedback as explicit goals.
- It avoids external reward models and iterative rollouts used in online RL.
- It does not require paired preference data like DPO.
- SFT collapses graded feedback into binary supervision.
- DPO depends on paired preference data that is often unavailable.
- The paper is available on arXiv with ID 2605.16345v1.
- The method trains purely through supervised learning.
Entities
Institutions
- arXiv