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Bibliographic Details
Main Authors: Jha, Basab, Paudel, Firoj, Puri, Ujjwal, Henkel, Ethan, Yuting, Zhang, Kowalczyk, Mateusz, Huang, Mei, Donghyuk, Choi, Junhao, Wang
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2601.04237
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Table of Contents:
  • 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