Summary of Q-sft: Q-learning For Language Models Via Supervised Fine-tuning, by Joey Hong et al.
Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning
by Joey Hong, Anca Dragan, Sergey Levine
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel offline RL algorithm addresses the challenges in scaling value-based methods for multi-turn RL to large language models. By casting Q-learning as a modified supervised fine-tuning (SFT) problem, it smoothly transitions from pretraining to learning a near-optimal Q-function during finetuning. This approach enjoys performance bounds similar to state-of-the-art Q-learning methods and utilizes an objective that closely resembles SFT. The algorithm can benefit from the full pretraining of language models without reinitializing weights or initializing new heads for predicting values or advantages. Empirically, it is evaluated on both pretrained LLMs and VLMs, tackling various tasks including natural language dialogue and robotic manipulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to use large language models for decision-making in complex situations. They wanted to teach these models to make good choices based on previous experiences, even when they don’t have all the information upfront. The proposed method is designed to work well with large models that have been pre-trained on lots of data and then fine-tuned for specific tasks. This approach helps the model learn from its past experiences without needing to relearn everything from scratch each time. The team tested their method on various tasks, such as conversing in natural language and controlling robots. |
Keywords
» Artificial intelligence » Fine tuning » Pretraining » Supervised
