Summary of Teaching Large Language Models to Reason with Reinforcement Learning, by Alex Havrilla et al.
Teaching Large Language Models to Reason with Reinforcement Learning
by Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, Christoforos Nalmpantis, Jane Dwivedi-Yu, Maksym Zhuravinskyi, Eric Hambro, Sainbayar Sukhbaatar, Roberta Raileanu
First submitted to arxiv on: 7 Mar 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 explores the performance of multiple reinforcement learning algorithms, including Expert Iteration, Proximal Policy Optimization (PPO), and Return-Conditioned RL, on improving language model reasoning capabilities. The algorithms learn from feedback provided to the language models, either heuristically or via a learned reward model. The study investigates the impact of sparse and dense rewards, as well as the effect of different model sizes and initializations with and without supervised fine-tuning (SFT) data. The results show that all algorithms perform similarly, with Expert Iteration performing best in most cases. Interestingly, the sample complexity of Expert Iteration is comparable to that of PPO, requiring around 10^6 samples to converge from a pre-trained checkpoint. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how different reinforcement learning (RL) algorithms can improve language models’ reasoning abilities by learning from human feedback. The researchers tested various algorithms and found that they all perform similarly well, with one algorithm called Expert Iteration doing the best in most cases. They also discovered that some RL algorithms require a lot of training data to work well. |
Keywords
* Artificial intelligence * Fine tuning * Language model * Optimization * Reinforcement learning * Supervised
