In the rapidly evolving field of large language models (LLMs), researchers and organizations face numerous challenges. These include enhancing reasoning capabilities, providing robust multilingual support, and effectively managing complex open-ended tasks. While smaller models are often more accessible and cost-effective, they typically underperform larger models. Therefore, developing medium-sized models that effectively balance computational efficiency with strong reasoning and instruction-following abilities has become a current trend.
Recently, Tsinghua University released GLM4, specifically the GLM-Z1-32B-0414 variant, addressing these challenges. Trained on a massive dataset comprising 15 trillion tokens, GLM4 aims to provide reliable multilingual capabilities and introduces an innovative reasoning strategy called "thinking-in-the-loop".
This release positions GLM4 alongside other prominent models like DeepSeek Distill, QwQ, and O1-mini, and is distributed under the popular MIT license. Notably, despite its 3.2 billion parameter size, GLM4 demonstrates performance in reasoning benchmarks comparable to much larger models such as GPT-4o and DeepSeek-V3, which boast up to 671 billion parameters.
Technically, GLM-Z1-32B-0414 leverages high-quality training data, including synthetically generated reasoning tasks, to enhance its analytical capabilities. The model integrates advanced techniques like rejection sampling and reinforcement learning (RL) to improve performance on agent-based tasks, coding, function calling, and search-driven question answering.
Furthermore, its "deep reasoning model" variant, optimized for complex mathematical, logical, and coding tasks, incorporates a cold-start method and extended RL training. A pairwise ranking feedback mechanism was also employed during training to enhance overall reasoning effectiveness.
An advanced variant, GLM-Z1-Rumination-32B-0414, introduces a novel "rumination" method, allowing the model to engage in extended, reflective reasoning to tackle open-ended, complex problems like AI-driven city analysis. This variant combines advanced search tools with multi-objective reinforcement learning, significantly improving its practicality in research-intensive tasks and complex retrieval scenarios. To cater to diverse needs, the GLM-Z1-9B-0414 version, with its 9 billion parameters, demonstrates strong mathematical and general reasoning capabilities, showcasing the viability of smaller-scale models.
Benchmark performance data underscores the strengths of the GLM4 family. Specifically, GLM-4-32B-0414 exhibits robust performance across multiple benchmarks, comparing favorably against models like GPT-4o, DeepSeek-V3, and Qwen2.5-Max. On the IFEval instruction-following benchmark, GLM4 achieved a high score of 87.6. On the TAU-Bench for task automation benchmarks such as retail (68.7) and aviation (51.2), GLM4 also performed well. In search-augmented question answering tasks evaluated by SimpleQA, the model achieved a score of 88.1.
Additionally, in function calling tasks on the BFCL-v3 benchmark, GLM4 achieved an overall score of 69.6, nearly matching GPT-4o's performance. In real-world code repair scenarios tested via the Moatless framework, GLM4 demonstrated a success rate of 33.8%, highlighting its practical value.
GLM4 demonstrates the potential as an effective LLM family, successfully bridging the performance gap between smaller, accessible models and traditionally larger ones. The GLM-Z1 series, particularly the 32B variant, exemplifies this balanced approach by providing strong reasoning capabilities while maintaining computational affordability. Its permissive MIT license positions GLM4 as a valuable tool for high-performance AI solutions in research and enterprise applications, without the substantial computational overhead associated with traditional large models.
huggingface:https://huggingface.co/THUDM/GLM-Z1-32B-0414
Key Highlights:
- 🌍 GLM4 is a 3.2 billion parameter language model released by Tsinghua University, featuring strong multilingual and reasoning capabilities.
- 📊 The model excels in various benchmark tests, particularly in instruction following and task automation, showcasing performance comparable to much larger models.
- 🚀 GLM4's MIT license makes high-performance AI solutions more accessible, suitable for research and enterprise applications.