New frontier models have developed the capability of eval-awareness, putting the utility of evals at risk. But what do people really mean when they say “a model is eval-aware?” In this post, I try to disentangle this statement and offer my views on the different concepts it entails. When people...
TL;DR For our capstone project in ARENA 6.0 (Sep 2025), we tried to create a model organism of encoded reasoning from Qwen3-4B using RL. Specifically, we tried to make the model solve math questions from the GSM8K using a chain-of-thought that kept its accuracy but led other judge models to...
New frontier models have developed the capability of eval-awareness, putting the utility of evals at risk. But what do people really mean when they say “a model is eval-aware?” In this post, I try to disentangle this statement and offer my views on the different concepts it entails.
When people say “model X is eval-aware”, I believe what they generally mean is “model X can tell when a task belongs to an evaluation, leading it to change its behaviour and propensities”.[1] Thus, this statement makes three claims about the model;
For our capstone project in ARENA 6.0 (Sep 2025), we tried to create a model organism of encoded reasoning from Qwen3-4B using RL. Specifically, we tried to make the model solve math questions from the GSM8K using a chain-of-thought that kept its accuracy but led other judge models to the wrong answer. We fell short of the ultimate goal due to (a lot of) reward hacking, sparse rewards, insufficient time (5 days), and this project being our first major exposure to RL. The goal of this write-up is to share our idea and takeaways, and document our ARENA capstone project experimental journey. We plan on continuing this work in the near future.
A... (read 2456 more words →)