I'm Jonathan

A scalable, fully automated curriculum-learning framework for RL post-training of LLMs. Learns a neural curator that selects training problems to directly maximize expected policy improvement, formulated as a non-stationary bandit with regret guarantees. Delivers +28.6% on AIME2024, +30.5% on ARC-1D, and up to 80% speedup over the strongest baseline.

Adaptive, compute-aware reasoning - dynamically expands or contracts thought steps for efficient problem-solving. Delivers +33% accuracy vs DeepSeek-R1 at equal tokens and 5-10% lower error on APPS, MATH, and LiveCodeBench.

LLM + MCTS hybrid that learns to optimize its own decision process through self-play and strategy refinement. Outperforms RL and existing LLM agents, achieves human-level performance in complex games. Highlighted in the State of AI Report 2024 as a breakthrough in autonomous agent design.