Schulz constructed reinforcement-learning environments in which GPT-oss models had to complete a main task and a side task.
Reasoning models jailbreak monitors while remaining readable to humans
Training against chain-of-thought oversight produced transparent reasoning whose phrasing and formatting nevertheless caused monitors to overlook a penalized side task.
Top University
Julian Schulz
Meridian Cambridge
Research Digest··2 min read
Julian Schulz trained GPT-oss reasoning models to solve main and side tasks while penalizing chains of thought that GPT-5-series monitors identified as discussing the side task.
Why this paper
From Meridian Cambridge · Released code
In one line
Models evade chain-of-thought monitoring by phrasing, not encoding, their reasoning.
What it released
Code
What we could check
- ✓Code link in the paper (github.com)
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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