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Meet VLM-CaR (Code as Reward): A New Machine Learning Framework Empowering Reinforcement Learning with Vision-Language Models

Researchers from Google DeepMind have collaborated with Mila, and McGill University defined appropriate reward functions to address the challenge of efficiently training reinforcement learning (RL) agents. The reinforcement learning method uses a rewarding system for achieving desired behaviors and punishing undesired ones. Hence, designing effective reward functions is crucial for RL agents to learn efficiently, but it often requires significant effort from environment designers. The paper proposes leveraging Vision-Language Models (VLMs) to automate the process…

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This Machine Learning Research Introduces Premier-TACO: A Robust and Highly Generalizable Representation Pretraining Framework for Few-Shot Policy Learning

In our ever-evolving world, the significance of sequential decision-making (SDM) in machine learning cannot be overstated. Unlike static tasks, SDM reflects the fluidity of real-world scenarios, spanning from robotic manipulations to evolving healthcare treatments. Much like how foundation models in language, such as BERT and GPT, have transformed natural language processing by leveraging vast textual data, pretrained foundation models hold similar promise for SDM. These models imbued with a rich understanding of decision sequences, can…

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Can Machine Learning Teach Robots to Understand Us Better? This Microsoft Research Introduces Language Feedback Models for Advanced Imitation Learning

The challenges in developing instruction-following agents in grounded environments include sample efficiency and generalizability. These agents must learn effectively from a few demonstrations while performing successfully in new environments with novel instructions post-training. Techniques like reinforcement learning and imitation learning are commonly used but often demand numerous trials or costly expert demonstrations due to their reliance on trial and error or expert guidance. In language-grounded instruction following, agents receive instructions and partial observations in the…

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Can Machine Learning Models Be Fine-Tuned More Efficiently? This AI Paper from Cohere for AI Reveals How REINFORCE Beats PPO in Reinforcement Learning from Human Feedback

The alignment of Large Language Models (LLMs) with human preferences has become a crucial area of research. As these models gain complexity and capability, ensuring their actions and outputs align with human values and intentions is paramount. The conventional route to this alignment has involved sophisticated reinforcement learning techniques, with Proximal Policy Optimization (PPO) leading the charge. While effective, this method comes with its own challenges, including high computational demands and the need for delicate…

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