Poster
in
Workshop: LAW 2025: Bridging Language, Agent, and World Models for Reasoning and Planning

Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers

Xingyue Huang ⋅ Rishabh ⋅ Gregor Franke ⋅ Ziyi Yang ⋅ Jiamu Bai ⋅ Weijie Bai ⋅ Jinhe Bi ⋅ Zifeng Ding ⋅ Yiqun Duan ⋅ Chengyu Fan ⋅ Wendong Fan ⋅ Xin Gao ⋅ Ruohao Guo ⋅ Yuan He ⋅ Yicheng He ⋅ Xianglong Hu ⋅ Neil Johnson ⋅ Bowen Li ⋅ Fangru Lin ⋅ Siyu Lin ⋅ Tong Liu ⋅ Yunpu Ma ⋅ HAO SHEN ⋅ Hao Sun ⋅ Beibei Wang ⋅ Fangyijie Wang ⋅ Hao Wang ⋅ Haoran Wang ⋅ Yang Wang ⋅ Yifeng Wang ⋅ Zhaowei Wang ⋅ Ziyang Wang ⋅ Yifan Wu ⋅ Zikai Xiao ⋅ Chengxing Xie ⋅ Fan Yang ⋅ Junxiao Yang ⋅ Qianshuo Ye ⋅ Ziyu Ye ⋅ Guangtao Zeng ⋅ Yuwen Zhang ⋅ Zeyu Zhang ⋅ Zihao Zhu ⋅ Bernard Ghanem ⋅ Philip Torr ⋅ Guohao Li

Project Page [ OpenReview]

Abstract

Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RLVR), particularly in domains like mathematics and programming, where ground-truth correctness can be automatically evaluated. However, extending this success to other reasoning-intensive domains remains challenging due to the scarcity of high-quality, verifiable datasets and the high cost of human supervision. In this work, we introduce the Loong Project: an open-source framework for scalable synthetic data generation and verification across a diverse range of reasoning-intensive domains. The framework consists of two key components: (1) LoongBench, a curated seed dataset containing 8,729 human-vetted examples across 12 domains (e.g., Advanced Mathematics, Chemistry, Logic), each paired with executable code and rich metadata; and (2) LoongEnv, a modular synthetic data generation environment that supports multiple prompting strategies to produce new question-answer-code triples. Together, these components form an agent-environment loop that enables reinforcement learning, where an LLM-based agent is rewarded for generating Chain-of-Thought (CoT) solutions that align with code-executed answers. Empirically, we benchmark LoongBench on a broad suite of both open-source and proprietary LLMs to evaluate domain coverage and reveal performance bottlenecks. In addition, we conduct a comprehensive analysis of synthetic data generated by LoongEnv, examining correctness, difficulty, and diversity. Code and documentation are available at https://github.com/camel-ai/loong.

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