Summary of Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation, by Meng Cao et al.
Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation
by Meng Cao, Lei Shu, Lei Yu, Yun Zhu, Nevan Wichers, Yinxiao Liu, Lei Meng
First submitted to arxiv on: 14 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed framework utilizes Large Language Models (LLMs) to generate intermediate-step rewards during reinforcement learning (RL) training, addressing the sparsity challenge in RL. The method couples a policy model with a critic LLM providing comprehensive feedback on each output part. This feedback is translated into token-level rewards guiding the RL process. Two settings are explored: one pairing a smaller policy model with a more powerful critic, and another where a single language model fulfills both roles. Experimental results show that incorporating artificial intrinsic rewards improves sample efficiency and overall performance of the policy model, supported by automatic and human evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to make language models learn better from the feedback they get. Right now, these models mostly learn from the mistakes they make, but this approach is limited because there’s only one reward for the whole output. The new method uses two different models working together: one that decides what to do and another that gives feedback on each step of the process. This helps the model learn more efficiently and accurately. The researchers tested their idea on three tasks – making text sound more positive, removing biased language from a text, and summarizing long texts. They found that this new approach makes the models better at learning and producing good results. |
Keywords
» Artificial intelligence » Language model » Reinforcement learning » Token
