Reward-Rate Policy Gradient: Teaching Agentic RL to Pay for Execution Time
sherryyangML · x · 2026-10-14
A new paper proposes Reward-Rate Policy Gradient (RPG): optimizing reward per unit time under a semi-Markov Decision Process formulation instead of fixed-step returns.
Motivation: for autoresearch and ML-engineering agents, an action may take seconds or hours — a fixed-step MDP misses this distinction. RPG makes the policy explicitly trade off speed versus reward, fitting agent training where long-running actions dominate cost.
Related event: Time-Aware RL Method RPG Boosts Efficiency by 85.7% Over Vanilla RL(2 posts)→
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