Athanor: agent team autonomously post-trains Qwen3-4B-Base to match official instruct model
Researchers introduced Athanor, an open agent-native system for autonomous post-training research (AutoPostTrain), described in arXiv:2610.21055 (submitted 15 Oct 2026). In a 104-hour campaign, a team of four Claude Opus 5.5 agents, each on an eight-GPU node and given no research advice, raised Qwen3-4B-Base from 15.9 to 67.3 on a six-task development suite and from 9.2 to 48.2 on a held-out suite, versus 66.5 and 47.9 for the official Qwen3-4B-Instruct-2507. The authors say this is the first demonstration that a team of frontier agents can autonomously post-train a raw base model into a cross-domain generalist that matches or surpasses the official instruct version on average. The campaign record shows the agents reached this level by recombining known methods: SFT on public reasoning data, RL with a rollout change, cross-agent weight merges and rounds of self-distillation.
If reproducible, it suggests today's frontier agents already have meaningful autonomous research and engineering ability, offering a public, measurable benchmark for automated AI research.