Prototypical Reward Network for Data-Efficient RLHF
- URL: http://arxiv.org/abs/2406.06606v2
- Date: Sun, 7 Jul 2024 16:29:17 GMT
- Title: Prototypical Reward Network for Data-Efficient RLHF
- Authors: Jinghan Zhang, Xiting Wang, Yiqiao Jin, Changyu Chen, Xinhao Zhang, Kunpeng Liu,
- Abstract summary: A reward model for Reinforcement Learning from Human Feedback (RLHF) has proven effective in fine-tuning Large Language Models (LLMs)
Our proposed framework Proto-RM leverages prototypical networks to enhance reward models under limited human feedback.
- Score: 17.220998116937444
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The reward model for Reinforcement Learning from Human Feedback (RLHF) has proven effective in fine-tuning Large Language Models (LLMs). Notably, collecting human feedback for RLHF can be resource-intensive and lead to scalability issues for LLMs and complex tasks. Our proposed framework Proto-RM leverages prototypical networks to enhance reward models under limited human feedback. By enabling stable and reliable structural learning from fewer samples, Proto-RM significantly enhances LLMs' adaptability and accuracy in interpreting human preferences. Extensive experiments on various datasets demonstrate that Proto-RM significantly improves the performance of reward models and LLMs in human feedback tasks, achieving comparable and usually better results than traditional methods, while requiring significantly less data. in data-limited scenarios. This research offers a promising direction for enhancing the efficiency of reward models and optimizing the fine-tuning of language models under restricted feedback conditions.
Related papers
- Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs [25.011675414622392]
Reward models trained on human preference data have been proven to be effective for aligning Large Language Models with human intent.
However, the generalization capabilities of current reward models to unseen prompts and responses are limited.
Our study proposes a novel approach to enhance the reward model's generalization ability against distribution shifts by regularizing the hidden states.
arXiv Detail & Related papers (2024-06-14T17:49:59Z) - Self-Exploring Language Models: Active Preference Elicitation for Online Alignment [90.4820014819937]
We propose a bilevel objective optimistically biased towards potentially high-reward responses to actively explore out-of-distribution regions.
Our experimental results demonstrate that when finetuned on Zephyr-7B-SFT and Llama-3-8B-Instruct models, SELM significantly boosts the performance on instruction-following benchmarks.
arXiv Detail & Related papers (2024-05-29T17:59:07Z) - Multi-Reference Preference Optimization for Large Language Models [56.84730239046117]
We introduce a novel closed-form formulation for direct preference optimization using multiple reference models.
The resulting algorithm, Multi-Reference Preference Optimization (MRPO), leverages broader prior knowledge from diverse reference models.
Our experiments demonstrate that LLMs finetuned with MRPO generalize better in various preference data, regardless of data scarcity or abundance.
arXiv Detail & Related papers (2024-05-26T00:29:04Z) - Towards Understanding the Influence of Reward Margin on Preference Model Performance [8.891183078634786]
This study introduces a novel method to estimate the preference differences without the need for detailed, exhaustive labels from human annotators.
Our experimental results provide empirical evidence that incorporating margin values into the training process significantly improves the effectiveness of reward models.
arXiv Detail & Related papers (2024-04-07T12:10:04Z) - Improving Reinforcement Learning from Human Feedback with Efficient Reward Model Ensemble [67.4269821365504]
Reinforcement Learning from Human Feedback (RLHF) is a widely adopted approach for aligning large language models with human values.
However, RLHF relies on a reward model that is trained with a limited amount of human preference data.
We contribute a reward ensemble method that allows the reward model to make more accurate predictions.
arXiv Detail & Related papers (2024-01-30T00:17:37Z) - Secrets of RLHF in Large Language Models Part II: Reward Modeling [134.97964938009588]
We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset.
We also introduce contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses.
arXiv Detail & Related papers (2024-01-11T17:56:59Z) - SALMON: Self-Alignment with Instructable Reward Models [80.83323636730341]
This paper presents a novel approach, namely SALMON, to align base language models with minimal human supervision.
We develop an AI assistant named Dromedary-2 with only 6 exemplars for in-context learning and 31 human-defined principles.
arXiv Detail & Related papers (2023-10-09T17:56:53Z) - Direct Preference Optimization: Your Language Model is Secretly a Reward
Model [126.78737228677025]
We introduce a new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form.
The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight.
Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods.
arXiv Detail & Related papers (2023-05-29T17:57:46Z) - RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment [32.752633250862694]
Generative foundation models are susceptible to implicit biases that can arise from extensive unsupervised training data.
We introduce a new framework, Reward rAnked FineTuning, designed to align generative models effectively.
arXiv Detail & Related papers (2023-04-13T18:22:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.