Summary of Exploring Rl-based Llm Training For Formal Language Tasks with Programmed Rewards, by Alexander G. Padula and Dennis J.n.j. Soemers
Exploring RL-based LLM Training for Formal Language Tasks with Programmed Rewards
by Alexander G. Padula, Dennis J.N.J. Soemers
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel approach to training large language models (LLMs) is presented in this paper, focusing on direct reinforcement learning from explicitly programmed reward signals. The authors explore the feasibility of using Proximal Policy Optimization (PPO) for tasks expressed through formal languages like mathematics and programming. They apply this method to three tasks: sentiment alignment, simple arithmetic, and game synthesis. While PPO is commonly used in Reinforcement Learning from Human Feedback, this study shows that direct RL-based training is challenging, even for simple tasks. The authors propose a novel regularization term to aid exploration, but note that training is not yet entirely stable. The findings suggest that LLMs may be more suitable for minor changes than learning new tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) can learn from explicit reward signals programmed by humans! In this study, researchers tried using a technique called Proximal Policy Optimization (PPO) to train LLMs. They focused on tasks that use formal languages like math and programming to test the model’s performance. The results show that it’s hard for LLMs to learn from these rewards alone, even for simple tasks. To help the models explore more, the authors suggested a new way to regularize their training. Overall, this study shows that LLMs might be better at making small changes than learning entirely new things. |
Keywords
» Artificial intelligence » Alignment » Optimization » Regularization » Reinforcement learning » Reinforcement learning from human feedback
