# Reinforcement Fine-tuning

Edge AI development has relied on developers hand-building fixed agent workflows that struggle with heterogeneous data modalities and diverse task pipelines. SE2 distills development into four atomic operations, retrieve, reason, code and test, and lets a coordinator agent pick the next one from the current context, while mobile runtime feedback and reinforcement fine-tuning let the agents keep evolving. On the IoTBench benchmark and three multimodal edge AI applications, its synthesized programs reach up to 26% higher accuracy and 28% lower resource consumption than eight state-of-the-art baselines.

Edge AI development has relied on developers hand-building fixed agent workflows that struggle with heterogeneous data modalities and diverse task pipelines. SE2 distills development into four atomic operations, retrieve, reason, code and test, and lets a coordinator agent pick the next one from the current context, while mobile runtime feedback and reinforcement fine-tuning let the agents keep evolving. On the IoTBench benchmark and three multimodal edge AI applications, its synthesized programs reach up to 26% higher accuracy and 28% lower resource consumption than eight state-of-the-art baselines.

In GRPO reinforcement fine-tuning, a fixed easy-to-hard curriculum can drift from the model's actual progress. ClosedLoopGaussian keeps the Gaussian sampler's form but uses periodic probe accuracy to adjust how fast training advances and how widely it rehearses, leaving the reward, architecture and optimizer unchanged. On Countdown, mean exact-match accuracy over four seeds exceeds both the fixed curriculum and vanilla GRPO, with gains concentrated at intermediate difficulty, though seed dispersion is large and the comparison is read descriptively.

In GRPO reinforcement fine-tuning, a fixed easy-to-hard curriculum can drift from the model's actual progress. ClosedLoopGaussian keeps the Gaussian sampler's form but uses periodic probe accuracy to adjust how fast training advances and how widely it rehearses, leaving the reward, architecture and optimizer unchanged. On Countdown, mean exact-match accuracy over four seeds exceeds both the fixed curriculum and vanilla GRPO, with gains concentrated at intermediate difficulty, though seed dispersion is large and the comparison is read descriptively.