5 citations · 7 across the 3 of their papers we have counts for
5 papers
Outcome-Routed Distillation and Reinforcement Learning for Diffusion Language Models
Wentao Lu, Tianyu Zhu, Jesse Clark
On-policy distillation provides dense supervision on the student's own samples, but its targets still come from the teacher. Verifier reinforcement learning (RL) instead uses an ou…
Context-Tower Conversion Preserves Generation While Freezing Retains Knowledge: Low-Budget AR-to-Diffusion Conversion of MoE LLMs
Wentao Lu, Jesse Clark, Tianyu Zhu
Converting a pretrained autoregressive (AR) model to a diffusion language model (dLLM) enables parallel generation without pretraining a new model. Published conversion methods dif…
Designing Interfaces for Multimodal Vector Search Applications
Owen Pendrigh Elliott, Tom Hamer, Jesse Clark
Multimodal vector search offers a new paradigm for information retrieval by exposing numerous pieces of functionality which are not possible in traditional lexical search engines.…
The Impacts of Data, Ordering, and Intrinsic Dimensionality on Recall in Hierarchical Navigable Small Worlds
Owen Pendrigh Elliott, Jesse Clark
Vector search systems, pivotal in AI applications, often rely on the Hierarchical Navigable Small Worlds (HNSW) algorithm. However, the behaviour of HNSW under real-world scenarios…
Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking
Tianyu Zhu, Myong Chol Jung, Jesse Clark
Contrastive learning has gained widespread adoption for retrieval tasks due to its minimal requirement for manual annotations. However, popular training frameworks typically learn…