Experience-Handoff Reinforcement Learning for Recursive Self-Improvement
The method trains an agent’s playbook-writing by measuring whether it helps future attempts succeed.
Scalar rewards leave out detailed feedback such as error logs. The researchers use a fixed Agentic Context Engineering harness and train one model to act, reflect and curate task-local playbooks. Their method evaluates those handoffs through their effect on later actor rollouts, jointly optimizing all three roles. The authors report higher held-out performance than baseline methods across agentic benchmarks; the abstract gives no numerical gains.
HF Daily Papers' note
Scalar rewards leave out detailed feedback such as error logs. The researchers use a fixed Agentic Context Engineering harness and train one model to act, reflect and curate task-local playbooks. Their method evaluates those handoffs through their effect on later actor rollouts, jointly optimizing all three roles. The authors report higher held-out performance than baseline methods across agentic benchmarks; the abstract gives no numerical gains.
HF Daily Papers' note
score 5