SAGE-32B: Agentic Reasoning via Iterative Distillation
For AI researchers and practitioners, SAGE-32B offers a specialized model for agentic tasks with improved planning and tool use, though it is an incremental improvement over existing methods.
SAGE-32B is a 32B parameter language model designed for agentic reasoning and long-range planning, achieving higher success rates in multi-tool usage scenarios on benchmarks like MMLU-Pro, AgentBench, and MATH-500 compared to similarly sized baselines.
We demonstrate SAGE-32B, a 32 billion parameter language model that focuses on agentic reasoning and long range planning tasks. Unlike chat models that aim for general conversation fluency, SAGE-32B is designed to operate in an agentic loop, emphasizing task decomposition, tool usage, and error recovery. The model is initialized from the Qwen2.5-32B pretrained model and fine tuned using Iterative Distillation, a two stage training process that improves reasoning performance through rigorously tested feedback loops. SAGE-32B also introduces an inverse reasoning approach, which uses a meta cognition head to forecast potential failures in the planning process before execution. On agentic reasoning benchmarks including MMLU-Pro, AgentBench, and MATH-500, SAGE-32B achieves higher success rates in multi tool usage scenarios compared to similarly sized baseline models, while remaining competitive on standard reasoning evaluations. Model weights are publicly released at https://huggingface.co/sagea-ai/sage-reasoning-32b