Learn Before
Action in the Context of LLMs
When applying reinforcement learning to Large Language Models, an action, denoted as , corresponds to a possible decision the agent can make. Specifically, an action represents a predicted token chosen from the model's vocabulary.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Action in the Context of LLMs
A simple robotic arm is being trained to sort objects on a conveyor belt. The arm can perform only three distinct movements from its resting position: it can pick up an object, it can place an object in a bin, or it can do nothing and wait. In this learning scenario, what does the set {pick up, place, wait} represent?
Evaluating an Agent's Action Set
Smart Thermostat Agent Actions
Learn After
Policy Formula for LLMs in Reinforcement Learning
A language model is generating a response to the prompt 'The best way to learn a new skill is to...'. So far, it has produced the sequence 'The best way to learn a new skill is to practice'. At this exact point in the generation process, what constitutes the model's next 'action' within a reinforcement learning framework?
Comparing 'Action' in Different Reinforcement Learning Scenarios
Identifying the Action in LLM Fine-Tuning