# RSI Arena

RSI Arena hands language-model post-training to ten frontier models: each agent starts from the same 30-billion-parameter base, picks its own data, writes its own training code and nominates checkpoints within six days and 1,000 GPU-hours. All ten produced working assistants, and the model trained by GPT-6 Astra closed a median 76% of the base-to-Instruct gap across 12 capability benchmarks; with answer length and formatting controlled, its human-preference rating is level with NVIDIA Instruct, 1,095 against 1,094. A second stage returned each model's blind battle records to its agent, and eight of the ten models improved their style-controlled rating relative to NVIDIA Instruct, while benchmark gains stayed modest and showed no clear link to preference gains.

RSI Arena hands language-model post-training to ten frontier models: each agent starts from the same 30-billion-parameter base, picks its own data, writes its own training code and nominates checkpoints within six days and 1,000 GPU-hours. All ten produced working assistants, and the model trained by GPT-6 Astra closed a median 76% of the base-to-Instruct gap across 12 capability benchmarks; with answer length and formatting controlled, its human-preference rating is level with NVIDIA Instruct, 1,095 against 1,094. A second stage returned each model's blind battle records to its agent, and eight of the ten models improved their style-controlled rating relative to NVIDIA Instruct, while benchmark gains stayed modest and showed no clear link to preference gains.