We have 3 of 16 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
On-policy self-distillation (OPSD) supplies dense token-level feedback on trajectories sampled from the student's own policy, a richer training signal than the outcome-level rewards of reinforcement learning. This feedback comes from a teacher conditioned on a full reference solution unavailable to the student. The ref...
CCubicSplat is a differentiable vector rasterizer that replaces B\'ezier closest-point solvers with uniform polyline surrogates whose geometric error is bounded at $O(S^{-2})$.
AD-Reranker is proposed, a novel framework that shifts reranker training from proxy imitation to answer-driven utility optimization, and reformulate the reranker as an environment-grounded agent that interacts with a downstream reader, modeled as a deterministic environment.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
