Liang Wen

Liang Wen, Yunke Cai, Fenrui Xiao et al.

This paper introduces Light-R1, an open-source suite for training long reasoning models using reproducible and cost-effective methodology. Given the proprietary nature of data used in the DeepSeek-R1 series, we develop an alternative approach leveraging exclusively public data and models. Our curriculum training progressively increases data difficulty, combined with multi-staged post-training. Our Light-R1-32B model, trained from Qwen2.5-32B-Instruct, outperforms DeepSeek-R1-Distill-Qwen-32B in math reasoning. Experimental results show that this curriculum approach becomes more effective when distinct, diverse datasets are available for different training stages: fine-tuning DeepSeek-R1-Distilled models (pre-tuned by DeepSeek team on proprietary data) with 3,000 challenging examples from our curriculum dataset yielded state-of-the-art 7B and 14B models, while the 32B model, Light-R1-32B-DS performed comparably to QwQ-32B and DeepSeek-R1. Furthermore, we extend our work by applying GRPO on long reasoning models. Our final Light-R1-14B-DS achieves SOTA performance among 14B models in math, with AIME24 & 25 scores of 74.0 and 60.2 respectively, surpassing many 32B models and DeepSeek-R1-Distill-Llama-70B. Despite math-focused training, Light-R1-14B-DS demonstrates strong cross-domain generalization. Light-R1 represents a significant advancement in making sophisticated reasoning models more accessible and implementable in real-world applications. Our models, training data and code have been made available at https://github.com/Qihoo360/Light-R1.

Boyan Xu, Liang Wen, Zihao Li et al.

Recent advancements in Large Language Models (LLMs) have sparked interest in their potential applications across various fields. This paper embarked on a pivotal inquiry: Can existing LLMs effectively serve as "water expert models" for water engineering and research tasks? This study was the first to evaluate LLMs' contributions across various water engineering and research tasks by establishing a domain-specific benchmark suite, namely, WaterER. Herein, we prepared 983 tasks related to water engineering and research, categorized into "wastewater treatment", "environmental restoration", "drinking water treatment and distribution", "sanitation", "anaerobic digestion" and "contaminants assessment". We evaluated the performance of seven LLMs (i.e., GPT-4, GPT-3.5, Gemini, GLM-4, ERNIE, QWEN and Llama3) on these tasks. We highlighted the strengths of GPT-4 in handling diverse and complex tasks of water engineering and water research, the specialized capabilities of Gemini in academic contexts, Llama3's strongest capacity to answer Chinese water engineering questions and the competitive performance of Chinese-oriented models like GLM-4, ERNIE and QWEN in some water engineering tasks. More specifically, current LLMs excelled particularly in generating precise research gaps for papers on "contaminants and related water quality monitoring and assessment". Additionally, they were more adept at creating appropriate titles for research papers on "treatment processes for wastewaters", "environmental restoration", and "drinking water treatment". Overall, this study pioneered evaluating LLMs in water engineering and research by introducing the WaterER benchmark to assess the trustworthiness of their predictions. This standardized evaluation framework would also drive future advancements in LLM technology by using targeting datasets, propelling these models towards becoming true "water expert".

Chenyang Wang, Liang Wen, Shousheng Jia et al.

While advancements in the reasoning abilities of LLMs have significantly enhanced their performance in solving mathematical problems, coding tasks, and general puzzles, their effectiveness in accurately adhering to instructions remains inconsistent, particularly with more complex directives. Our investigation identifies lazy reasoning during the thinking stage as the primary factor contributing to poor instruction adherence. To mitigate this issue, we propose a comprehensive framework designed to enable rigorous reasoning processes involving preview and self-checking, essential for satisfying strict instruction constraints. Specifically, we first generate instructions with complex constraints and apply a filtering process to obtain valid prompts, resulting in three distinct prompt datasets categorized as hard, easy, and pass. Then, we employ rejection sampling on the pass prompts to curate a small yet high-quality dataset, enabling a cold-start initialization of the model and facilitating its adaptation to effective reasoning patterns. Subsequently, we employ an entropy-preserving supervised fine-tuning (Entropy-SFT) strategy coupled with token-wise entropy-adaptive (TEA-RL) reinforcement learning guided by rule-based dense rewards. This approach encourages the model to transform its reasoning mechanism, ultimately fostering generalizable reasoning abilities that encompass preview and self-checking. Extensive experiments conducted on instruction-following benchmarks demonstrate remarkable performance improvements across various model scales. Notably, our Light-IF-32B model surpasses both larger open-source models such as DeepSeek-R1 and closed-source models like Doubao-1.6.