{"ID":2822920,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.04237","arxiv_id":"2601.04237","title":"SAGE-32B: Agentic Reasoning via Iterative Distillation","abstract":"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","short_abstract":"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 i...","url_abs":"https://arxiv.org/abs/2601.04237","url_pdf":"https://arxiv.org/pdf/2601.04237v2","authors":"[\"Basab Jha\",\"Firoj Paudel\",\"Ujjwal Puri\",\"Ethan Henkel\",\"Zhang Yuting\",\"Mateusz Kowalczyk\",\"Mei Huang\",\"Choi Donghyuk\",\"Wang Junhao\"]","published":"2026-01-04T16:41:58Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CL\",\"cs.LG\"]","methods":"[\"Language Model\"]","has_code":false}