8 papers
TAROT: Test-driven and Capability-adaptive Curriculum Reinforcement Fine-tuning for Code Generation with Large Language Models
Chansung Park, Juyong Jiang, Fan Wang +4
Large Language Models (LLMs) are changing the coding paradigm, known as vibe coding, yet synthesizing algorithmically sophisticated and robust code still remains a critical challen…
Diagnosing Knowledge Conflict in Multimodal Long-Chain Reasoning
Jing Tang, Kun Wang, Haolang Lu +7
Multimodal large language models (MLLMs) in long chain-of-thought reasoning often fail when different knowledge sources provide conflicting signals. We formalize these failures und…
Klear-AgentForge: Forging Agentic Intelligence through Posttraining Scaling
Qi Wang, Hongzhi Zhang, Jia Fu +12
Despite the proliferation of powerful agentic models, the lack of critical post-training details hinders the development of strong counterparts in the open-source community. In thi…
Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning
Wu Fei, Hao Kong, Shuxian Liang +5
Process Reinforcement Learning~(PRL) has demonstrated considerable potential in enhancing the reasoning capabilities of Large Language Models~(LLMs). However, introducing additiona…
LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to Small-Scale Local LLMs
Chansung Park, Juyong Jiang, Fan Wang +2
The widespread adoption of cloud-based proprietary large language models (LLMs) has introduced significant challenges, including operational dependencies, privacy concerns, and the…
A Survey on Mixture of Experts in Large Language Models
Weilin Cai, Juyong Jiang, Fan Wang +3
Large language models (LLMs) have garnered unprecedented advancements across diverse fields, ranging from natural language processing to computer vision and beyond. The prowess of…