Multi-Fidelity Policy Gradients Stabilize Data-Scarce Reinforcement Learning
Quick summary
arXiv:2610.02505v1 Announce Type: cross Abstract: Policy gradient methods for on-policy reinforcement learning (RL) can become unstable when expensive, scarce target-domain data yield noisy gradient estimates. We address this challenge by complementing limited high-fidelity (HF) target-domain data with abundant, cheap, but biased low-fidelity (LF) data, e.g., from a simplified simulator. Most existing methods directly optimize biased objectives based on LF data. In contrast, the recently introduced multi-fidelity policy gradient (MFPG) framework uses LF data solely to construct a control varia
Key takeaways
- arXiv:2610.02505v1 Announce Type: cross Abstract: Policy gradient methods for on-policy reinforcement learning (RL) can become unstable when expensive, scarce target-domain data yield noisy gradient estimates.
- We address this challenge by complementing limited high-fidelity (HF) target-domain data with abundant, cheap, but biased low-fidelity (LF) data, e.g., from a simplified simulator.
- Most existing methods directly optimize biased objectives based on LF data.
Why it matters
“Multi-Fidelity Policy Gradients Stabilize Data-Scarce Reinforcement Learning” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
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