8 citations · 8 across the 8 of their papers we have counts for
8 papers
Scaling Test-Time Compute for Agentic Coding
Joongwon Kim, Wannan Yang, Kelvin Niu +13
Test-time scaling has become a powerful way to improve large language models. However, existing methods are best suited to short, bounded outputs that can be directly compared, ran…
Prompt Curriculum Learning for Efficient LLM Post-Training
Zhaolin Gao, Joongwon Kim, Wen Sun +4
We introduce Prompt Curriculum Learning (PCL), a lightweight reinforcement learning (RL) algorithm that selects intermediate-difficulty prompts using a learned value model to post-…
ASTRO: Teaching Language Models to Reason by Reflecting and Backtracking In-Context
Joongwon Kim, Anirudh Goyal, Liang Tan +3
We introduce ASTRO, the "Autoregressive Search-Taught Reasoner", a framework for training language models to reason like search algorithms, explicitly leveraging self-reflection, b…
A Systematic Examination of Preference Learning through the Lens of Instruction-Following
Joongwon Kim, Anirudh Goyal, Aston Zhang +6
Preference learning is a widely adopted post-training technique that aligns large language models (LLMs) to human preferences and improves specific downstream task capabilities. In…
Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning
Joongwon Kim, Bhargavi Paranjape, Tushar Khot +1
Language agents perform complex tasks by using tools to execute each step precisely. However, most existing agents are based on proprietary models or designed to target specific ta…
TaskWeb: Selecting Better Source Tasks for Multi-task NLP
Joongwon Kim, Akari Asai, Gabriel Ilharco +1
Recent work in NLP has shown promising results in training models on large amounts of tasks to achieve better generalization. However, it is not well-understood how tasks are relat…
Induce, Edit, Retrieve: Language Grounded Multimodal Schema for Instructional Video Retrieval
Yue Yang, Joongwon Kim, Artemis Panagopoulou +2
Schemata are structured representations of complex tasks that can aid artificial intelligence by allowing models to break down complex tasks into intermediate steps. We propose a n…
BiSECT: Learning to Split and Rephrase Sentences with Bitexts
Joongwon Kim, Mounica Maddela, Reno Kriz +2
An important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary.…