53 citations · 177 across the 34 of their papers we have counts for
8 papers · 1 filter
GAV: A scientific experimentation environment for PDE agents
Muheng Li, Wenlong Mou
Reliable feedback is essential for coding agents to develop programs for partial differential equations (PDEs). Yet evaluating these programs on new PDE problems often requires exp…
A Latent Distribution Perspective on Evaluating and Improving Latent Generative Models
Xianghong Fang, Wenjie Shu, Tongda Xu +3
In latent generative models, reconstruction quality is often assumed to correlate with generative performance. However, reconstruction FID (rFID) can exhibit weak or even negative…
Adaptively Incorporating Directional Hints into Zeroth-Order Optimization
Alexander Ryabchenko, Jian Qian, Wenlong Mou
We study zeroth-order optimization of non-convex functions with the aid of directional hints, which are cheap but potentially inaccurate approximations of the true gradient directi…
What should post-training optimize? A test-time scaling law perspective
Muheng Li, Jian Qian, Wenlong Mou
Large language models are increasingly deployed with test-time strategies: sample responses, score them with a reward model or verifier, and return the best. This deployment ru…
Provable imitation learning for control of instability in partially-observed Vlasov--Poisson equations
Xiaofan Xia, Qin Li, Wenlong Mou
We consider the stabilization of Vlasov--Poisson plasma dynamics, a central control problem in nuclear fusion. Our focus is the gap between what an ideal controller would use and w…
Revisiting the Constant Stepsize Stochastic Approximation with Decision-Dependent Markovian Noise
Hadi Hadavi, Wenlong Mou, Sergey Samsonov +1
We revisit the convergence analysis of constant stepsize stochastic approximation (SA) with decision-dependent Markovian noise, with a focus on characterizing the stationary bias a…
Continuous-time reinforcement learning: ellipticity enables model-free value function approximation
Wenlong Mou
We study off-policy reinforcement learning for controlling continuous-time Markov diffusion processes with discrete-time observations and actions. We consider model-free algorithms…
Predicting and improving test-time scaling laws via reward tail-guided search
Muheng Li, Jian Qian, Wenlong Mou
Test-time scaling has emerged as a critical avenue for enhancing the reasoning capabilities of Large Language Models (LLMs). Though the straight-forward ''best-of-'' (BoN) strat…