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 Vision-Language Preference learning framework, named VLP, which learns a vision-language preference model to provide 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. The model learns to extract language-related features, and then serves as a predictor in various downstream tasks. The policy can be learned according to the annotated labels 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 and shifting the burden from continuous, per-task human annotation to one-time, per-domain data collection.

Large language models (LLMs) have revolutionized various domains with their remarkable capabilities, but their massive parameter sizes pose significant challenges for fine-tuning and inference, especially in resource-constrained environments. Conventional compression methods often result in substantial performance degradation within LLMs and struggle to restore model quality during fine-tuning. To address this challenge, we present Bayesian Knowledge Distillation (BayesKD), a novel distillation framework meticulously designed for compact LLMs in resource-constrained fine-tuning scenarios. Departing from conventional LLM distillation methods that introduce time-consuming paradigms and fail to generalize in compressed LLM fine-tuning scenarios, our BayesKD develops the Logits Dual-Scaling, Knowledge Alignment Module, and Bayesian Distillation Optimization. In particular, our Logits Dual-Scaling strategy adaptively aligns the strength of the teacher’s knowledge transfer, while the Knowledge Alignment Module bridges the gap between the teacher and student models by projecting their knowledge representations into a shared interval. Additionally, we employ Logits-Aware Bayesian Optimization to swiftly identify optimal settings based on these strategies, thereby enhancing model performance. Extensive experiments across diverse tasks demonstrate that BayesKD consistently outperforms baseline methods on various state-of-the-art LLMs, including LLaMA, Qwen2, Bloom, and Vicuna. Notably, our BayesKD achieves average accuracy gains of 2.99% and 4.05% over standard KD for the 8B parameter LLaMA and Qwen2 model. Codes are available in the supplementary materials.