Topic
Cameron R Wolfe
2 episodes
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Agentic Rl
Cameron Wolfe's 'Agentic RL' argues that training LLMs for agentic work requires shifting from single-turn reasoning to multi-turn trajectory optimization, where the harness (tools, environment, memory) becomes part of the RL loop itself. The central claim is that standard post-training methods fail on long-horizon tasks because they don't account for environment state changes across steps, necessitating new rollout infrastructures and stability techniques like PPO over GRPO for variable-length traces.
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Agent Evals
Justy and Cody dig into Cameron Wolfe’s argument that agent evals need to move from static benchmark thinking to realistic harnesses that test autonomy, tool use, recovery, and long-horizon behavior. They get specific about the agentic loop, why tool-call correctness is only part of the story, and where outcome-based evals can hide ugly behavior. Cody mostly buys the technical framing, with caveats about overfitting to harnesses and the difficulty of defining ground truth trajectories. Justy keeps pulling it back to who actually needs this now: teams shipping coding, workflow, or other higher-stakes agents where a demo is not the same as reliability.