← Back to all news
arXiv Machine Learning By Keerthana Gurushankar

Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources

Read the original on arXiv Machine Learning →

The paper introduces Lag, a mean‑field optimal index policy for online assignment problems with reusable resources, such as peer review, call routing, ridesharing, emergency dispatch, and crowdsourcing. By modeling the problem as a contextual restless multi‑armed bandit and deriving an occupancy‑measure linear program, the authors obtain Lag from the dual and prove it is asymptotically optimal in a scaling regime where server copies and arrivals grow together. Experiments on peer‑review and call‑center data show Lag achieves 83–99% of the LP upper bound and outperforms greedy assignment by up to 36% in synthetic settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Oct 9

Amortized Off-Policy Evaluation for LLMs

The paper introduces PFN‑OPE, a prior‑data fitted network that performs amortized off‑policy evaluation (OPE) for large language models (LLMs). Unlike traditional OPE methods that must be retrained for each new logged dataset or reward definition, PFN‑OPE is pretrained once on a diverse set of contextual‑bandit tasks and can estimate value for any new logged dataset and target response in a single forward pass. Experiments on HelpSteer2 and UltraFeedback with Qwen, Llama, and Gemma policies show that PFN‑OPE reduces error by 2.0 to 9.3 times compared to the best baselines in reward‑shifted settings.

By Younwoo Choi, Leo Feng, Vincent Liu, Haanvid Lee
llmsreinforcement-learning
More like this →