I created an LLM post-training method called RPS. Preliminary results show that it improved Qwen3-8b's program synthesis reliability. [R]

Reddit r/MachineLearning Tools
post-training curriculum-learning learning-rate-decay program-synthesis qwen llm-training

Summary

RPS is a two-stage LLM post-training method inspired by neuroscience, combining curriculum learning with learning rate decay. Preliminary results show improved program synthesis reliability on Qwen3-8b compared to equal learning rate training.

RPS is inspired by neuroscience. As humans, we learn basic skills as kids with high neuro-plasticity. We then learn advanced skills as teens and adults with low neuro-plasticity. RPS trains a model in 2 stages. In stage 1, the model is trained on easy data with high learning rate. In stage 2, the model is trained on hard data with 10% the learning rate of stage 1. RPS is basically a combination of existing ideas: curriculum learning + learning rate decay. ARC-AGI 1 public eval scores: base model: Qwen3-8b RPS: 4% EPS (equal learning rate in both stages): 2.4% Program Synthesis Stats: Program executions without error: RPS: 1145/1200 EPS: 870/1200 [https://iamjasonfeng.blogspot.com/2026/05/regressive-plasticity-schedule.html](https://iamjasonfeng.blogspot.com/2026/05/regressive-plasticity-schedule.html) [https://github.com/iamjasonfeng/RPS](https://github.com/iamjasonfeng/RPS)
Original Article

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