13 citations · 43 across the 29 of their papers we have counts for
7 papers · 1 filter
Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following
Qingyu Ren, Qianyu He, Powei Chang +5
Language models often struggle to follow multi-constraint instructions that are crucial for real-world applications. Existing reinforcement learning (RL) approaches suffer from dep…
Beyond the Trade-off: Self-Supervised Reinforcement Learning for Reasoning Models' Instruction Following
Qingyu Ren, Qianyu He, Bowei Zhang +6
Reasoning models excel in complex problem solving but exhibit a concerning trade off between reasoning capabilities and instruction following abilities. Existing approaches for imp…
ChemAmp: Amplified Chemistry Tools via Composable Agents
Zhucong Li, Powei Chang, Jin Xiao +6
Although LLM-based agents are proven to master tool orchestration in scientific fields, particularly chemistry, their single-task performance remains limited by underlying tool con…
Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following
Jie Zeng, Qianyu He, Qingyu Ren +5
Real-world instructions with multiple constraints pose a significant challenge to existing large language models (LLMs). An observation is that the LLMs exhibit dramatic performanc…
ChemHTS: Hierarchical Tool Stacking for Enhancing Chemical Agents
Zhucong Li, Jin Xiao, Bowei Zhang +5
Large Language Models (LLMs) have demonstrated remarkable potential in scientific research, particularly in chemistry-related tasks such as molecular design, reaction prediction, a…
Order Doesn't Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation
Qianxi He, Qianyu He, Jiaqing Liang +4
Logical reasoning is essential for large language models (LLMs) to ensure accurate and coherent inference. However, LLMs struggle with reasoning order variations and fail to genera…
Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of Large Language Models
Qingyu Ren, Jie Zeng, Qianyu He +5
It is crucial for large language models (LLMs) to follow instructions that involve multiple constraints. However, it is an unexplored area to enhance LLMs' ability to follow soft c…