Summary of Imitating Language Via Scalable Inverse Reinforcement Learning, by Markus Wulfmeier et al.
Imitating Language via Scalable Inverse Reinforcement Learning
by Markus Wulfmeier, Michael Bloesch, Nino Vieillard, Arun Ahuja, Jorg Bornschein, Sandy Huang, Artem Sokolov, Matt Barnes, Guillaume Desjardins, Alex Bewley, Sarah Maria Elisabeth Bechtle, Jost Tobias Springenberg, Nikola Momchev, Olivier Bachem, Matthieu Geist, Martin Riedmiller
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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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 use of inverse reinforcement learning (IRL) as an alternative to maximum likelihood estimation (MLE) for training large language models. The authors propose a new approach that combines IRL with MLE, allowing for a trade-off between complexity and performance. They find that IRL-based imitation leads to more diverse and high-performing generations in the supervised fine-tuning setting, making it a strong alternative even without online data generation. The authors also analyze the benefits of IRL-extracted reward functions, finding advantages for more robust reward functions via tighter integration of supervised and preference-based LLM post-training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using a new way to train language models called inverse reinforcement learning (IRL). Right now, most language model training uses imitation learning. Imitation learning makes the model learn by copying what it sees or hears. The new approach combines two methods: IRL and maximum likelihood estimation (MLE). This combination lets us control how complex the model is and how well it performs. The results show that using IRL gives us more diverse and better language generations, even without adding new data. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Likelihood » Reinforcement learning » Supervised
