Jasper's open RL guide: shaping rewards to train a search agent end-to-end
simonguozirui · x · 2026-09-19
Jasper published an educational blog post on RL for LLMs, using a search agent as the case study because every lever affects model behavior in legible ways. It shows how small reward-function updates teach the model to avoid sloppy tool calls, prune unnecessary docs, and balance persistence with token efficiency. All rollouts are browsable, the code is open source, and the post walks through learning-rate sweeps to reward shaping.
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