Agents
Studying Sutton and Barto's RL book and its connections to RL for LLMs (e.g., tool use, math reasoning, agents, and so on)? [D]
A Reddit discussion thread on r/MachineLearning in which practitioners explore how foundational concepts from Sutton and Barto's *Reinforcement Learning: An Introduction* — including MDPs, policy g...
A Reddit discussion thread on r/MachineLearning in which practitioners explore how foundational concepts from Sutton and Barto's Reinforcement Learning: An Introduction — including MDPs, policy gradients, and reward maximization — map to modern RL applications for large language models (LLMs), such as RLHF, tool use, mathematical reasoning, and agentic behavior. The thread serves as a community resource for learners seeking to bridge classical RL theory with contemporary LLM alignment and agent research. Commenters discuss study strategies, supplementary resources, and which specific Sutton & Barto chapters are most relevant to understanding techniques like PPO-based fine-tuning and multi-step reasoning in LLM pipelines.
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Source: agents