12 citations · 12 across the 6 of their papers we have counts for
6 papers
Latent Action Reparameterization for Efficient Agent Inference
Wenhao Huang, Qingwen Zeng, Qiyue Chen +11
Large language model (LLM) agents often rely on long sequences of low-level textual actions, resulting in large effective decision horizons and high inference cost. While prior wor…
Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs
Yuxuan Lu, Ziyi Wang, Yingzhou Lu +12
Training tool-calling agents requires large-scale trajectory data with verifiable labels, yet existing approaches either synthesize environments that diverge from real API behavior…
Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction
Yong Lin, Shange Tang, Bohan Lyu +17
We introduce Goedel-Prover-V2, a series of open-source language models that set a new state-of-the-art in automated theorem proving. Built on the standard expert iteration and rein…
Does the Order of Fine-tuning Matter and Why?
Qihong Chen, Jiawei Li, Hyunjae Suh +5
To improve the performance on a target task, researchers have fine-tuned language models with an intermediate task before the target task of interest. However, previous works have…
Beyond Self-learned Attention: Mitigating Attention Bias in Transformer-based Models Using Attention Guidance
Jiri Gesi, Iftekhar Ahmed
Transformer-based models have demonstrated considerable potential for source code modeling tasks in software engineering. However, they are limited by their dependence solely on au…
Code Smells in Machine Learning Systems
Jiri Gesi, Siqi Liu, Jiawei Li +6
As Deep learning (DL) systems continuously evolve and grow, assuring their quality becomes an important yet challenging task. Compared to non-DL systems, DL systems have more compl…