works on

From the 1 of 5 linked papers with an AI index.

activity
20242026
collaborators

5 papers

cs.LG2026

Beyond the Best Teacher: Expanding and Compressing the Reasoning Solution Manifold

Songshuo Lu, Zhi Chen, Yaohua Tang

The paper proposes an expand‑then‑compress framework that builds a diverse set of RL‑trained teacher models and then distills them into a single student model, improving reasoning,…

cs.CV2026

MusaCoder: Native GPU Kernel Generation with Full-Stack Training on Moore Threads GPU

Kun Cheng, Songshuo Lu, Sicong Liao +7

Native GPU kernel generation turns high-level tensor programs into executable, efficient low-level code. Existing Large Language Models (LLMs) struggle with this task, while execut…

cs.CV2025

URPO: A Unified Reward & Policy Optimization Framework for Large Language Models

Songshuo Lu, Hua Wang, Zhi Chen +1

Large-scale alignment pipelines typically pair a policy model with a separately trained reward model whose parameters remain frozen during reinforcement learning (RL). This separat…

cs.CL2024

A Full-duplex Speech Dialogue Scheme Based On Large Language Models

Peng Wang, Songshuo Lu, Yaohua Tang +3

We present a generative dialogue system capable of operating in a full-duplex manner, allowing for seamless interaction. It is based on a large language model (LLM) carefully align…

cs.CV2024

TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text

Songshuo Lu, Hua Wang, Yutian Rong +2

Current Retrieval-Augmented Generation (RAG) systems concatenate and process numerous retrieved document chunks for prefill which requires a large volume of computation, therefore…