INTELLECT-3
Free while signed in. Answers cite the passages they came from.
INTELLECT-3 is a 106B-parameter Mixture-of-Experts model (12B active) trained with large-scale reinforcement learning, achieving state-of-the-art performance for its size across math, code, science, and reasoning benchmarks. Built on top of GLM-4.5-Air base, it outperforms many larger frontier models, including DeepSeek R1-0528, and matches GLM-4.6 (which has over 3x the parameters) on key benchmarks.
Frontier benchmark results: Achieves 90.8% on AIME 2024 and 88.0% on AIME 2025, outperforming DeepSeek R1-0528. Scores 69.3% on LiveCodeBench v6, beating GLM-4.5-Air by 8%. Competitive on GPQA Diamond (74.4%), HLE (14.6%), and MMLU-Pro (81.9%).
prime-rl framework: Introduces an open-source asynchronous RL framework with disaggregated trainer and inference, continuous batching with in-flight weight updates, and native support for multi-turn agentic rollouts. Scales seamlessly from single node to 512 H200 GPUs.
Two-stage post-training: Combines supervised fine-tuning on over 200B tokens of reasoning traces (from datasets like OpenReasoning-Math/Code/Science) with large-scale RL across diverse environments, including math, code, science, logic, deep research, and software engineering tasks.
Verifiers and Environments Hub: Open-sources the complete training infrastructure, including the verifiers library for environment design, an Environments Hub with 500+ contributed RL environments, and Prime Sandboxes for high-throughput secure code execution supporting over 4,000 concurrent sandboxes.
Full reproducibility: Releases model weights, complete training recipe, RL framework, and all environments used for training and evaluation. Training ran on 512 H200s over two months with a batch size of 256 prompts and 16 rollouts per prompt at 65K context length.
Get next week’s papers.
The same picks and the same summaries, in your inbox. Free, and 176 issues deep.
Subscribe on Substack