Training Data

• Agent Learning via Early Experience
  A long-term goal of language agents is to learn and improve through their own experience, ultimately outperforming humans in complex, real-world tasks. However, training agents from experience data wi…
• LIMI: Less is More for Agency
  We define “Agency” as the emergent capacity of AI systems to function as autonomous agents—actively discovering problems, formulating hypotheses, and executing solutions through self-directed engageme…
• LSR: Reinforcement Learning with Supervised Reward Outperforms SFT in Instruction Following
  After the pretraining stage of LLMs, techniques such as SFT, RLHF, RLVR, and RFT are applied to enhance instruction-following ability, mitigate undesired responses, improve reasoning capability and en…
• Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning
  Large Language Models (LLMs) have demonstrated significant improvements in reasoning capabilities through supervised fine-tuning and reinforcement learning. However, when training reasoning models, th…
• MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis
  The application of AI in psychiatric diagnosis faces significant challenges, including the subjective nature of mental health assessments, symptom overlap across disorders, and privacy constraints lim…
• Orchestrating Synthetic Data with Reasoning
  Many AI applications of interest require specialized multi-modal models. Yet, relevant data for training these models is inherently scarce. Human annotation is prohibitively expensive, error-prone, an…
• Reasoning to Learn from Latent Thoughts
  Human-written text is the culmination of an underlying thought process—when we write, there is often an internal dialogue that clarifies or even determines the written word. However, modern language m…
• Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
  Synthetic data generation with Large Language Models (LLMs) has emerged as a promising paradigm for augmenting natural data over a nearly infinite range of tasks. However, most existing methods are fa…
• TarGEN: Targeted Data Generation with Large Language Models
  We present TarGEN, a multi-step prompting strategy for generating high-quality synthetic datasets using LLMs. An advantage of TarGEN is its seedless nature; it does not require specific task instances…