Summary of Regressing the Relative Future: Efficient Policy Optimization For Multi-turn Rlhf, by Zhaolin Gao et al.
Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF
by Zhaolin Gao, Wenhao Zhan, Jonathan D. Chang, Gokul Swamy, Kianté Brantley, Jason D. Lee, Wen Sun
First submitted to arxiv on: 6 Oct 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 Large Language Models (LLMs) have made significant progress in single-turn tasks like summarization. However, they struggle with multi-turn tasks like dialogue that require long-term planning. To address this issue, we introduce REgressing the RELative FUture (REFUEL), an efficient policy optimization approach for LLMs. REFUEL employs a single model to estimate Q-values and trains on self-generated data, addressing the covariate shift issue. Theoretically, we prove that REFUEL can match the performance of any policy covered by the training set. Empirically, we evaluate our algorithm using Llama-3.1-70B-it and show it consistently outperforms state-of-the-art methods across various settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are really good at summarizing text, but they struggle with conversations that go back and forth. This paper introduces a new way to make these models better at long conversations. It’s called REFUEL, which is short for “REgressing the RELative FUture”. The idea is to use a single model to figure out what’s important in a conversation and train it on its own data, not just human feedback. This makes the model better at understanding the conversation as it goes along. In tests, this new approach worked really well and was even able to beat more powerful models on some tasks. |
Keywords
» Artificial intelligence » Llama » Optimization » Summarization
