A Critical Look At Tokenwise Reward-Guided Text Generation

Ahmad Rashid, Ruotian Wu, Julia Grosse, Hongliang Li, Agustinus Kristiadi, Pascal Poupart

Keywords: LLM, RLHF, Alignment, Model Efficiency, Reward Models, Sampling

TL;DR: We analyse some of the pitfalls of contemporary reward guided text generation methods, and present a principled approach with strong performance on several language generation benchmarks.

Abstract: Large language models (LLMs) can be improved by aligning with human preferences through fine-tuning---the so-called reinforcement learning from human feedback (RLHF). However, the cost of fine-tuning an LLM is prohibitive for many users. Due to their ability to bypass LLM fine-tuning, prediction-time tokenwise reward-guided text generation (RGTG) methods have recently been proposed. They use a reward model trained on full sequences to score partial sequences during decoding in a bid to steer the generation towards sequences with high rewards. However, these methods have so far been only heuristically motivated and poorly analyzed. In this work, we show that reward models trained on full sequences are not compatible with scoring partial sequences. To alleviate this issue, we propose to train a Bradley-Terry reward model on partial sequences explicitly, and autoregressively sample from the implied tokenwise policy during decoding time. We study the properties of this reward model and the resulting policy: We show that this policy is proportional to the ratio of two distinct RLHF policies. Our simple approach outperforms previous RGTG methods and performs similarly to strong offline baselines without large-scale LLM finetuning.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 8231