Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models
A seven-person independent team trained open-weight agentic cyber models via a data-centric post-training framework, ranking 10th on CyberGym and first at comparable scale.
The paper presents Feyospace-v1, a data-centric post-training framework combining five systems: Choulea (hidden reasoning analysis), SkyReal (teacher-sampling cost reduction), Hongzwang (bypassing teacher API restrictions), PSBreakup (restoring capabilities weakened by model merging), and Kreator (converting expert interventions into trainable reasoning). The data engine builds resettable coding, vulnerability, CTF, kernel-history, full-exploit, firmware, and device-backed environments, retaining only execution-verified and evidence-audited trajectories, yielding 164,269 trajectories for long-context supervised fine-tuning. Three checkpoints improve over their starting models by an average of 23.76% on the full CyberGym suite and 10.49% across pooled CTF suites. As of September 1, 2026, Feyospace-s1 achieves a 63.24% verified success rate, ranks 10th on the official CyberGym leaderboard, and all three checkpoints rank 1st among models at comparable parameter scales.
- Five-system framework cuts cost of executable environments and multi-turn supervision
- 164,269 execution-verified trajectories used for long-context supervised fine-tuning
- Checkpoints gain 23.76% on CyberGym and 10.49% on pooled CTF suites
- Feyospace-s1 hits 63.24% verified success, 10th on official CyberGym leaderboard
- First end-to-end open-weight frontier cyber training by a seven-person independent team
From the original article
1 min read at huggingface.co
Training capable cyber agents is often treated primarily as a problem of model scale, yet open-weight post-training is constrained more directly by the cost of executable environments, reliable multi-turn supervision, and access to strong teachers.
By Zongjie Li, Alan Z. W, John Nicolas J · huggingface.co
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