AIFrontierLab / AgentArk

What it does

• Single-agent efficiency, multi-agent reasoning. A distilled student matches most of the gain of a full debate ensemble at a fraction of the inference cost.
• PRM capacity matters more than student size. A stronger process reward model lifts smaller students disproportionately; student capacity bounds the multi-agent gain.
• Reasoning quality outweighs quantity. Curated, higher-fidelity trajectories beat naive scale-up of distillation data.
• Process-aware distillation improves reasoning behavior, not just accuracy. Students internalize critique-and-revise dynamics rather than memorizing answers.
• Robust and general. Gains transfer to out-of-distribution and robustness benchmarks (e.g., TruthfulQA).