52 citations · 69 across the 7 of their papers we have counts for
8 papers
ForkPilot: Self-Evolving Policy for Retrospective Search in Long-Horizon Agents
Xinyue Zeng, Shivam Shandilya, Guilherme Potje +6
Interactive language-model agents increasingly solve complex tasks through long-horizon, multi-call reasoning, where errors in beliefs or actions can compound across tool interacti…
Diagnosing Capability Gaps in Fine-Tuning Data
Saeid Asgari Taghanaki, Rakshanda Agarwal, Bruce Sun +10
Fine-tuning large language models (LLMs) for domain-specific tasks requires training datasets that comprehensively cover the target capabilities a practitioner needs. Yet identifyi…
SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing
Yifei Xu, Guilherme Potje, Shivam Shandilya +9
Designing aligned and robust rewards for open-ended generation remains a key barrier to RL post-training. Rubrics provide structured, interpretable supervision, but scaling rubric…
Direct Reasoning Optimization: Token-Level Reasoning Reflectivity Meets Rubric Gates for Unverifiable Tasks
Yifei Xu, Tusher Chakraborty, Srinagesh Sharma +6
Reinforcement learning (RL) training of large language models (LLMs) on unverifiable tasks is challenging even when a reasonable-quality reference answer is available. We propose a…
RLTHF: Targeted Human Feedback for LLM Alignment
Yifei Xu, Tusher Chakraborty, Emre Kıcıman +11
Fine-tuning large language models (LLMs) to align with user preferences is challenging due to the high cost of quality human annotations in Reinforcement Learning from Human Feedba…
Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning
Nick Mecklenburg, Yiyou Lin, Xiaoxiao Li +9
In recent years, Large Language Models (LLMs) have shown remarkable performance in generating human-like text, proving to be a valuable asset across various applications. However,…
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
Angels Balaguer, Vinamra Benara, Renato Luiz de Freitas Cunha +13
There are two common ways in which developers are incorporating proprietary and domain-specific data when building applications of Large Language Models (LLMs): Retrieval-Augmented…
GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models
Bruno Silva, Leonardo Nunes, Roberto Estevão +2
Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding across various domains, including healthcare and finance. For some tasks, L…