Reinforcement
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…
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…
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