A Long Way to Go: Investigating Length Correlations in RLHF
- URL: http://arxiv.org/abs/2310.03716v2
- Date: Wed, 10 Jul 2024 23:15:49 GMT
- Title: A Long Way to Go: Investigating Length Correlations in RLHF
- Authors: Prasann Singhal, Tanya Goyal, Jiacheng Xu, Greg Durrett,
- Abstract summary: This paper demonstrates, on three diverse settings, that optimizing for response length is a significant factor behind RLHF.
We find improvements in reward to largely be driven by increasing response length, instead of other features.
Even a purely length-based reward reproduces most downstream RLHF improvements over supervised fine-tuned models.
- Score: 59.49656695716066
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Great success has been reported using Reinforcement Learning from Human Feedback (RLHF) to align large language models, with open preference datasets enabling wider experimentation, particularly for "helpfulness" in tasks like dialogue and web question answering. Alongside these improvements, however, RLHF also often drives models to produce longer outputs. This paper demonstrates, on three diverse settings, that optimizing for response length is, much more than previously thought, a significant factor behind RLHF. Studying the strategies RL optimization uses to maximize reward, we find improvements in reward to largely be driven by increasing response length, instead of other features. Indeed, we find that even a purely length-based reward reproduces most downstream RLHF improvements over supervised fine-tuned models. Testing a comprehensive set of length-countering interventions, we identify the dominant source of these biases to be reward models, which, by studying training dynamics, we find are non-robust and easily influenced by length biases in preference data.
Related papers
- Disentangling Length from Quality in Direct Preference Optimization [93.74831404396174]
Reinforcement Learning from Human Feedback (RLHF) has been a crucial component in the recent success of Large Language Models.
RLHF is know to exploit biases in human preferences, such as verbosity.
We develop a principled but simple regularization strategy that prevents length exploitation, while still maintaining improvements in model quality.
arXiv Detail & Related papers (2024-03-28T06:03:47Z) - PERL: Parameter Efficient Reinforcement Learning from Human Feedback [27.687265760622918]
Reinforcement Learning from Human Feedback (RLHF) has proven to be a strong method to align Large Language Models with human preferences.
We study RLHF where the underlying models are trained using the parameter efficient method of Low-Rank Adaptation (LoRA) introduced by Hu et al.
We find that PERL performs on par with the conventional RLHF setting, while training faster, and with less memory.
arXiv Detail & Related papers (2024-03-15T21:43:46Z) - PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models [13.313186665410486]
Reward finetuning has emerged as a promising approach to aligning foundation models with downstream objectives.
Existing reward finetuning methods are limited by their instability in large-scale prompt datasets.
We propose Proximal Reward Difference Prediction (PRDP) to enable stable black-box reward finetuning for diffusion models.
arXiv Detail & Related papers (2024-02-13T18:58:16Z) - ODIN: Disentangled Reward Mitigates Hacking in RLHF [127.35607931337019]
We study the issue of reward hacking on the response length, a challenge emerging in Reinforcement Learning from Human Feedback.
A well-formatted, verbose but less helpful response from the LLMs can often deceive LLMs or even human evaluators to achieve high scores.
Our approach almost eliminates the reward correlation with length, and improves the obtained policy by a significant margin.
arXiv Detail & Related papers (2024-02-11T22:40:12Z) - 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) - The Alignment Ceiling: Objective Mismatch in Reinforcement Learning from
Human Feedback [5.037876196534672]
Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique to make large language models (LLMs) more capable in complex settings.
In this paper, we illustrate the causes of this issue, reviewing relevant literature from model-based reinforcement learning, and argue for solutions.
arXiv Detail & Related papers (2023-10-31T21:52:41Z) - Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning
from Human Feedback [55.78118035358662]
Reinforcement learning from human feedback serves as a crucial bridge, aligning large language models with human and societal values.
We have identified that the reward model often finds shortcuts to bypass its intended objectives.
We propose an innovative solution, applying the Product-of-Experts technique to separate reward modeling from the influence of sequence length.
arXiv Detail & Related papers (2023-10-08T15:14:39Z) - RRHF: Rank Responses to Align Language Models with Human Feedback
without tears [69.68672043223249]
InstructGPT implements RLHF through several stages, including Supervised Fine-Tuning (SFT), reward model training, and Proximal Policy Optimization (PPO)
We propose a novel learning paradigm called RRHF, which scores sampled responses from different sources via a logarithm of conditional probabilities.
We evaluate RRHF on the Helpful and Harmless dataset, demonstrating comparable alignment performance with PPO by reward model score and human labeling.
arXiv Detail & Related papers (2023-04-11T15:53:40Z)
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.