# 2026-06-04 23:18:15

FYI, my notes- I take the ariahw/rl-rewardhacking reward hacking setup and a 4B model- I extend from 1 to 4 hints+hacks- make a reward hacking vector from contrastive pairs collected on each LoRA module's weights from the GRPO gradients over the pairs. These pairs are synthetic and not in distribution. (Steering vectors from gradients are different, but this approach was actually published previously)- This vector now controls the routing SGTM style
One caveat: Since I lack a significant compute budget I avoided running 65-hour pure GRPO jobs and instead bootstrapped the process. 15% of samples come from a hacky teacher, and that makes the run <2 hours. However the result is basically the same because the student's GRPO generates more hacks than the teacher after 40 steps.