A Systematic Analysis of Base Model Choice for Reward Modeling (2025.emnlp-main)
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| Challenge: | Reinforcement learning from human feedback (RLHF) and reward modeling are key to training powerful large language models (LLMs). |
| Approach: | They propose to combine RLHF and reward modeling to boost model selection . they also demonstrate that a small set of benchmarks could be combined to boost the model selection. |
| Outcome: | The results show that the model selection can be improved by up to 14% compared to the most common (default) choice. |
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