AReaL vs chinese-llm-benchmark
AReaL: AReaL is a fully asynchronous reinforcement learning system from Tsinghua IIIS and Ant Group for training large reasoning and agentic models. Version 2.0 splits it into training, inference, agent and weight-update microservices, and black-box agent apps can be trained online by pointing their base_url at AReaL.; chinese-llm-benchmark: ReLE Benchmark (formerly CLiB) provides a continuously updated evaluation for Chinese AI large language models, covering over 337 commercial and open-source LLMs. It offers multi-dimensional capability assessments across various domains, along with comprehensive rankings and a large defect library for model improvement.
TL;DR
RL fine-tuning of reasoning models on math and coding tasks
Comparing and selecting the best performing LLMs for specific applications.
Side-by-Side Comparison
AReaLchinese-llm-benchmarkFeatures
AReaLchinese-llm-benchmarkUse Cases
AReaLchinese-llm-benchmarkBest For
AReaLchinese-llm-benchmarkFAQ
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