Summary of Salsa: Soup-based Alignment Learning For Stronger Adaptation in Rlhf, by Atoosa Chegini et al.
SALSA: Soup-based Alignment Learning for Stronger Adaptation in RLHF
by Atoosa Chegini, Hamid Kazemi, Iman Mirzadeh, Dong Yin, Maxwell Horton, Moin Nabi, Mehrdad Farajtabar, Keivan Alizadeh
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper presents SALSA, a novel approach to align Large Language Models with human values and preferences. Traditional Reinforcement Learning from Human Feedback (RLHF) relies on the Kullback-Leibler divergence between the current policy and a frozen initial policy, which limits exploration of the reward landscape. To overcome this limitation, SALSA creates a more flexible reference model through weight-space averaging of two independent supervised fine-tuned models, allowing for larger deviation in KL divergence and exploring a promising region of the solution space without sacrificing stability. This approach fosters better exploration, achieving higher rewards, improving model robustness, out-of-distribution generalization, and performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SALSA is a new way to help computers learn from people’s feedback. Right now, when we train computers to do tasks, they often get stuck in one way of doing things. SALSA helps them explore more options and find better ways to do tasks. This makes the computers better at understanding what people want them to do. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning from human feedback » Rlhf » Supervised
