3 accepted papers
Large Language Model (LLM) agents show great promise for complex multi-turn tool-use tasks, but their development is often hampered by the extreme scarcity of high-quality training data. Supervised fine-tuning (SFT) on synthetic data leads to overfitting, whereas standard reinforcement learning (RL)…
Large Language Models (LLMs), despite their remarkable capabilities, are prone to generating hallucinated or outdated content due to their static internal knowledge. While Retrieval-Augmented Generation (RAG) integrated with Reinforcement Learning (RL) offers a solution, these methods are fundament
Online matching with diversity and fairness pursuit, a common building block in the recommendation and advertising, can be modeled as constrained convex programming with high entropy. While most existing approaches are based on the “single slot” assumption (i.e., assigning one item per iteration), t…