1 citations · 1 across the 4 of their papers we have counts for
4 papers
Not All Denoising Steps Are Equal: Model Scheduling for Faster Masked Diffusion Language Models
Ivan Sedykh, Nikita Sorokin, Valentin Malykh
Recent advances in masked diffusion language models (MDLMs) narrow the quality gap to autoregressive LMs, but their sampling remains expensive because generation requires many full…
Hierarchical Embedding Fusion for Retrieval-Augmented Code Generation
Nikita Sorokin, Ivan Sedykh, Valentin Malykh
Retrieval-augmented code generation often conditions the decoder on large retrieved code snippets. This ties online inference cost to repository size and introduces noise from long…
Iterative Self-Training for Code Generation via Reinforced Re-Ranking
Nikita Sorokin, Ivan Sedykh, Valentin Malykh
Generating high-quality code that solves complex programming tasks is challenging, especially with current decoder-based models that produce highly stochastic outputs. In code gene…
Searching by Code: a New SearchBySnippet Dataset and SnippeR Retrieval Model for Searching by Code Snippets
Ivan Sedykh, Dmitry Abulkhanov, Nikita Sorokin +2
Code search is an important and well-studied task, but it usually means searching for code by a text query. We argue that using a code snippet (and possibly an error traceback) as…