RLAIF: Scaling Reinforcement Learning from Human Feedback with AI
Feedback
- URL: http://arxiv.org/abs/2309.00267v2
- Date: Fri, 1 Dec 2023 01:41:44 GMT
- Title: RLAIF: Scaling Reinforcement Learning from Human Feedback with AI
Feedback
- Authors: Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan
Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav
Rastogi, Sushant Prakash
- Abstract summary: Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences.
RL from AI Feedback (RLAIF) offers a promising alternative that leverages a powerful off-the-shelf LLM to generate preferences in lieu of human annotators.
Our results suggest that RLAIF can achieve human-level performance, offering a potential solution to the scalability limitations of RLHF.
- Score: 5.469395454378616
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Reinforcement learning from human feedback (RLHF) has proven effective in
aligning large language models (LLMs) with human preferences. However,
gathering high-quality human preference labels can be a time-consuming and
expensive endeavor. RL from AI Feedback (RLAIF), introduced by Bai et al.,
offers a promising alternative that leverages a powerful off-the-shelf LLM to
generate preferences in lieu of human annotators. Across the tasks of
summarization, helpful dialogue generation, and harmless dialogue generation,
RLAIF achieves comparable or superior performance to RLHF, as rated by human
evaluators. Furthermore, RLAIF demonstrates the ability to outperform a
supervised fine-tuned baseline even when the LLM preference labeler is the same
size as the policy. In another experiment, directly prompting the LLM for
reward scores achieves superior performance to the canonical RLAIF setup, where
LLM preference labels are first distilled into a reward model. Finally, we
conduct extensive studies on techniques for generating aligned AI preferences.
Our results suggest that RLAIF can achieve human-level performance, offering a
potential solution to the scalability limitations of RLHF.
Related papers
- Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments [41.25558612970942]
We show that large language models (LLMs) exhibit preference biases and worrying sensitivity to prompt designs.
Motivated by this phenomenon, we propose an automatic Zero-shot Evaluation-oriented Prompt Optimization framework, ZEPO.
arXiv Detail & Related papers (2024-06-17T09:48:53Z) - 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) - 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) - Aligning Large Language Models with Human Preferences through Representation Engineering [41.81020951061438]
Drawing inspiration from the emerging field of representation engineering (RepE), this study aims to identify relevant representations for high-level human preferences embedded in patterns of activity within an LLM.
This novel approach, denoted as Representation Alignment from Human Feedback (RAHF), proves to be effective, computationally efficient, and easy to implement.
arXiv Detail & Related papers (2023-12-26T11:01:36Z) - Nash Learning from Human Feedback [86.09617990412941]
We introduce an alternative pipeline for the fine-tuning of large language models using pairwise human feedback.
We term this approach Nash learning from human feedback (NLHF)
We present a novel algorithmic solution, Nash-MD, founded on the principles of mirror descent.
arXiv Detail & Related papers (2023-12-01T19:26:23Z) - 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) - Preference Ranking Optimization for Human Alignment [90.6952059194946]
Large language models (LLMs) often contain misleading content, emphasizing the need to align them with human values.
Reinforcement learning from human feedback (RLHF) has been employed to achieve this alignment.
We propose Preference Ranking Optimization (PRO) as an efficient SFT algorithm to fine-tune LLMs for human alignment.
arXiv Detail & Related papers (2023-06-30T09:07:37Z) - 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) - Fine-tuning Language Models with Generative Adversarial Reward Modelling [30.424363135421917]
Reinforcement Learning with Human Feedback (RLHF) has been demonstrated to significantly enhance the performance of large language models (LLMs)
We propose another alternative approach: Reinforcement Learning with Generative Adversarial Feedback (RLGAF) to RLHF and SFT.
arXiv Detail & Related papers (2023-05-09T17:06:06Z)
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.