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Hongli Yu

4 accepted papers

2026

MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent

ICLR 2026oral

Despite improvements by length extrapolation, efficient attention and memory modules, handling infinitely long documents without performance degradation during extrapolation remains the ultimate challenge in long-text processing. To solve this problem, We introduce a novel agent workflow, \method, w…

Cited by 0SourceScholar
2025

DAPO: An Open-Source LLM Reinforcement Learning System at Scale

NeurIPS 2025poster

Inference scaling empowers LLMs with unprecedented reasoning ability, with reinforcement learning as the core technique to elicit complex reasoning. However, key technical details of state-of-the-art reasoning LLMs are concealed (such as in OpenAI o1 blog and DeepSeek R1 technical report), thus the…

Cited by 0SourceScholar
2025

Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles

NeurIPS 2025spotlight

Large Language Models (LLMs), such as OpenAI’s o1 and DeepSeek’s R1, excel at advanced reasoning tasks like math and coding via Reinforcement Learning with Verifiable Rewards (RLVR), but still struggle with puzzles solvable by humans without domain knowledge. We introduce ENIGMATA, the first compreh…

Cited by 0SourcecodeScholar
2025

Retro-R1: LLM-based Agentic Retrosynthesis

NeurIPS 2025poster

Retrosynthetic planning is a fundamental task in chemical discovery. Due to the vast combinatorial search space, identifying viable synthetic routes remains a significant challenge--even for expert chemists. Recent advances in Large Language Models (LLMs), particularly equipped with reinforcement le…

Cited by 0SourceScholar