Framework
Process Reinforcement through Implicit Rewards (PRIME): A Scalable Machine Learning Framework for Enhancing Reasoning Capabilities
Reinforcement learning (RL) for large language models (LLMs) has traditionally relied on outcome-based rewards, which provide feedback only on the final output. This sparsity of reward makes it challenging to train models that need multi-step reasoning, like those employed in mathematical problem-solving and programming. Additionally, credit assignment becomes ambiguous, as the model does not get…
Updating the Frontier Safety Framework
Our next iteration of the FSF sets out stronger security protocols on the path to AGI AI is a powerful tool that is helping to unlock new breakthroughs and make significant progress on some of the biggest challenges of our time, from climate change to drug discovery. But as its development progresses, advanced capabilities may…
Introducing the Frontier Safety Framework
Our approach to analyzing and mitigating future risks posed by advanced AI models Google DeepMind has consistently pushed the boundaries of AI, developing models that have transformed our understanding of what’s possible. We believe that AI technology on the horizon will provide society with invaluable tools to help tackle critical global challenges, such as climate…
Google DeepMind Introduces MONA: A Novel Machine Learning Framework to Mitigate Multi-Step Reward Hacking in Reinforcement Learning
Reinforcement learning (RL) focuses on enabling agents to learn optimal behaviors through reward-based training mechanisms. These methods have empowered systems to tackle increasingly complex tasks, from mastering games to addressing real-world problems. However, as the complexity of these tasks increases, so does the potential for agents to exploit reward systems in unintended ways, creating new…
Microsoft AI Research Introduces MVoT: A Multimodal Framework for Integrating Visual and Verbal Reasoning in Complex Tasks
The study of artificial intelligence has witnessed transformative developments in reasoning and understanding complex tasks. The most innovative developments are large language models (LLMs) and multimodal large language models (MLLMs). These systems can process textual and visual data, allowing them to analyze intricate tasks. Unlike traditional approaches that base their reasoning skills on verbal means,…