VLP: Vision-Language Preference Learning for Embodied Manipulation
- URL: http://arxiv.org/abs/2502.11918v1
- Date: Mon, 17 Feb 2025 15:32:14 GMT
- Title: VLP: Vision-Language Preference Learning for Embodied Manipulation
- Authors: Runze Liu, Chenjia Bai, Jiafei Lyu, Shengjie Sun, Yali Du, Xiu Li,
- Abstract summary: We propose a vision-language preference model to provide preference feedback for embodied manipulation tasks.<n>The preference model learns to extract language-related features, and then serves as a preference annotator in various downstream tasks.<n>Our method provides accurate preferences and generalizes to unseen tasks and unseen language instructions, outperforming the baselines by a large margin.
- Score: 29.7387976970634
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Reward engineering is one of the key challenges in Reinforcement Learning (RL). Preference-based RL effectively addresses this issue by learning from human feedback. However, it is both time-consuming and expensive to collect human preference labels. In this paper, we propose a novel \textbf{V}ision-\textbf{L}anguage \textbf{P}reference learning framework, named \textbf{VLP}, which learns a vision-language preference model to provide preference feedback for embodied manipulation tasks. To achieve this, we define three types of language-conditioned preferences and construct a vision-language preference dataset, which contains versatile implicit preference orders without human annotations. The preference model learns to extract language-related features, and then serves as a preference annotator in various downstream tasks. The policy can be learned according to the annotated preferences via reward learning or direct policy optimization. Extensive empirical results on simulated embodied manipulation tasks demonstrate that our method provides accurate preferences and generalizes to unseen tasks and unseen language instructions, outperforming the baselines by a large margin.
Related papers
- MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time [50.41806216615488]
Large Language Models (LLMs) acquire extensive knowledge and remarkable abilities from extensive text corpora.
To make LLMs more usable, aligning them with human preferences is essential.
We propose an effective method, textbf MetaAlign, which aims to help LLMs dynamically align with various explicit or implicit preferences specified at inference time.
arXiv Detail & Related papers (2024-10-18T05:31:13Z) - Investigating on RLHF methodology [0.0]
We discuss the features of training a Preference Model, which simulates human preferences, and the methods and details we found essential for achieving the best results.
We also discuss using Reinforcement Learning to fine-tune Large Language Models and describe the challenges we faced and the ways to overcome them.
arXiv Detail & Related papers (2024-10-02T17:46:22Z) - Language Representations Can be What Recommenders Need: Findings and Potentials [57.90679739598295]
We show that item representations, when linearly mapped from advanced LM representations, yield superior recommendation performance.
This outcome suggests the possible homomorphism between the advanced language representation space and an effective item representation space for recommendation.
Our findings highlight the connection between language modeling and behavior modeling, which can inspire both natural language processing and recommender system communities.
arXiv Detail & Related papers (2024-07-07T17:05:24Z) - Personalized Language Modeling from Personalized Human Feedback [45.16986573937782]
Personalized large language models (LLMs) are designed to tailor responses to individual user preferences.<n>We propose Personalized-RLHF, an efficient framework that utilizes a lightweight user model to capture individual user preferences.<n>We show that personalized LLMs trained using P-RLHF generate responses that are more closely aligned with individual user preferences.
arXiv Detail & Related papers (2024-02-06T04:18:58Z) - Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback [70.32795295142648]
Linear alignment is a novel algorithm that aligns language models with human preferences in one single inference step.
Experiments on both general and personalized preference datasets demonstrate that linear alignment significantly enhances the performance and efficiency of LLM alignment.
arXiv Detail & Related papers (2024-01-21T10:46:23Z) - ULMA: Unified Language Model Alignment with Human Demonstration and
Point-wise Preference [16.73260713938154]
A typical alignment procedure consists of supervised fine-tuning and preference learning.
We introduce Point-wise Direct Preference Optimization, a novel preference learning method designed to harness point-wise feedback effectively.
Our work also uncovers a novel connection between supervised fine-tuning and point-wise preference learning, culminating in Unified Language Model Alignment.
arXiv Detail & Related papers (2023-12-05T07:52:12Z) - Sample Efficient Preference Alignment in LLMs via Active Exploration [63.84454768573154]
We take advantage of the fact that one can often choose contexts at which to obtain human feedback to most efficiently identify a good policy.
We propose an active exploration algorithm to efficiently select the data and provide theoretical proof that it has a worst-case regret bound.
Our method outperforms the baselines with limited samples of human preferences on several language models and four real-world datasets.
arXiv Detail & Related papers (2023-12-01T00:54:02Z) - Offline RL for Natural Language Generation with Implicit Language Q
Learning [87.76695816348027]
Large language models can be inconsistent when it comes to completing user specified tasks.
We propose a novel RL method, that combines both the flexible utility framework of RL with the ability of supervised learning.
In addition to empirically validating ILQL, we present a detailed empirical analysis situations where offline RL can be useful in natural language generation settings.
arXiv Detail & Related papers (2022-06-05T18:38:42Z) - Pre-Trained Language Models for Interactive Decision-Making [72.77825666035203]
We describe a framework for imitation learning in which goals and observations are represented as a sequence of embeddings.
We demonstrate that this framework enables effective generalization across different environments.
For test tasks involving novel goals or novel scenes, initializing policies with language models improves task completion rates by 43.6%.
arXiv Detail & Related papers (2022-02-03T18:55:52Z)
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.