MO' TURNS, MO' PROBLEMS

Summary from the TLDR AI newsletter

Doing reinforcement learning for agent use-cases is conceptually not at all that complicated. For each problem, you set up a VM with a harness and a fresh model context, send the prompt, let the model make tool calls, score the whole thing at the end, and update the weights. Agentic post-training means there's more than the model to consider. Even when the harness is consistent, you can still go off-policy with the trajectories themselves. Agents spawn subagents, make parallel tool calls, and compact large sessions, which adds complexity.