This post summarises our paper Learning Steganography Is Easy, Learning Steganographic Reasoning Is Hard, accepted as an oral at the NeurIPS 2026 Workshop on Trustworthy AI for Good (AI4GOOD). Code, configurations and results for all experiments: github.com/stegano-ai/steg-reasoning-is-hard.
This work was done as part of the Meridian Visiting Researcher Programme and the Safe AI Germany (SAIGE) Incubator Program, with funding from Coefficient Giving.

TL;DR

Chain-of-thought (CoT) monitoring fails if a model can reason steganographically,^[1]i.e., compute through text that reads as an innocuous answer to some other question. Two neighbouring skills, steganographic messaging (hiding provided values) and encoded reasoning (computing in an illegible but unconcealed format), have already been shown to emerge under reinforcement learning (RL) against a monitor.
We attempt to elicit all three behaviours under in-context learning, RL, and supervised fine-tuning (SFT). Steganographic messaging and encoded reasoning are learned readily under all three. Steganographic reasoning is not: in-context it never appears, under RL it plateaus at 69% encoding accuracy where messaging reaches nearly 100%, and under SFT it costs 2.4–20× the training messaging needs, if learned at all.
There is one exception: on a contrived knapsack task, where the cover text consists of arbitrary choices that require no reasoning, steganographic reasoning is learned under all three methods, including, for the larger of the two models we tested, RL from an outcome-only reward.
So: steganographic reasoning is much harder than its components, and demonstrations of the components should not be read as evidence for it. However, it is learnable on tasks made of arbitrary choices, and realistic agent transcripts plausibly contain many of those.

Figure 1. Steganographic reasoning is harder to learn than steganographic messaging or encoded reasoning. Top: messaging hides given values in innocuous text; encoded reasoning computes step by step in an illegible but unconcealed format; steganographic reasoning computes inside text that looks like an innocuous answer to an unrelated task. Bottom: steganographic reasoning (red) stays far behind under (a) in-context learning, (b) RL on a given scheme, and (c) SFT, even on deliberately simple tasks.

Steganographic reasoning would be a big deal, but it has not been shown to

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