Training fails to elicit subtle reasoning in current language models

Fabien Roger; Hoagy; gasteigerjo; Joe Benton; Vlad Mikulik

The output monitor views the final code solution, with the comments stripped. We strip the comments to prevent the model from using comments to justify blatant backdoors as benign, in order to make the task harder.

This feels like a pretty significant restriction, as you’re eliminating the model’s ability to use a form a “persuasion” in the output. What’s the rationale for this, given that most other changes seem to be framed as making the task favor the attacker?

Like given that “justify as benign” is in fact the way it circumvents the reasoning monitor:

We find that Sonnet 3.7 can learn to evade either a reasoning monitor, by persuading the monitor that a blatant backdoor is benign, or an output-only monitor, by devising sophisticated backdoors that the output-only monitor doesn’t detect. But when trained to evade both reasoning and output-only monitors, Sonnet 3.7 is unable to use reasoning to improve its backdoor success rate without triggering a reasoning monitor.

Was it using this to also evade the output monitor? If you ran any experiments without stripping comments, did it change your results?

[-]Fabien Roger1mo30

I prefer the setting we used for the purpose of studying CoT monitoring because it removes a class of "easy hacks" that probably don't require a CoT to craft (while I would guess that CoT is more helpful to craft more "structural" backdoors).

(I'll let @mishajw answer about experiments without stripping comments, I don't recall what he tried.)

[-]micahcarroll1mo10

Glad you published this post – very interesting work!

Looking at the monitor prompt in the appendix, one concern I have is that it seems pretty unrealistic that the monitor would be given (a large part of) the exact misalignment-prompt given to the main policy. Instead, I'd imagine a realistic monitoring setting to involve a pretty generic prompt to look for signs of misalignment and suspicious behavior, which include backdoors but would probably include many other bad behaviors. Do you think this may be unrealistically favoring the monitors in your experiments?

Relatedly, aren't you worried of overestimating the difficulty of subtle reasoning because of your choice of domain? Backdoor insertion seems more easily verifiable/monitorable than other behaviors which have more plausible deniability – as an example, it seems potentially much harder to detect sandbagging using a monitor once the explicit choice to sandbag has become obfuscated.

As a last question, you say that "while we interpret certain behaviors as persuasion based on the model justifying obvious backdoors, we cannot definitively rule out that the model is performing some other form of cognitive work.". Doesn't the fact that the subplot in Figure 2 (row 2, column 2) is increasing with more reasoning tokens (from about maybe ~70% to ~80%) suggest that some stenographic cognitive work is happening in these "persuasion CoTs", or at the very least the single-forward-pass backdoor capabilities of the policy are increasing over the course of training? Curious if you have reasons to discount this possible explanation!

Introduction

As models become more capable, we would like to guarantee that their deployment will not cause catastrophic harm. One potential path towards making this guarantee – at least for a subset of catastrophic harms – is to argue that we can detect and prevent catastrophes by monitoring a model’s reasoning (Korbak et al., 2025). Broadly, this argument runs as follows:

We outline a set of catastrophic threat models that we want to rule out.

For some subset of the threat models, we find that the model is capable of carrying these out with reasoning, but not without. The rest of the argument only applies for these threat models, and other threats will have to be handled independently.

We demonstrate that the reasoning is monitorable with respect to a given threat model: by inspecting the model’s reasoning, we are able to determine whether it is being used to achieve the threat.

Therefore, the model is safe to deploy if its reasoning is monitored for catastrophic threats.

In this work, we investigate the validity of step 3: whether inspecting a model’s reasoning allows us to determine whether it is achieving a given threat. However, it’s worth noting that the rest of the argument makes important assumptions that we don’t discuss here, such as: that the list of threat models is complete; that we have robustly demonstrated that these tasks rely on reasoning; that our monitoring is able to detect all outlined catastrophic risks; that we are able to prevent catastrophe as a result of a monitor triggering.

There are intuitive arguments for the monitorability of reasoning. First, models are extensively trained on human-written internet text, and thus any subsequent fine-tuning (for example using reinforcement learning from verifiable rewards) will start with a strong default bias towards reasoning in human-like text. Second, due to the lack of recurrence in modern language model architectures, sufficiently long sequential chains of reasoning must be encoded in the tokens produced by a model (Merrill et al., 2023); thus, for tasks requiring sufficiently long sequential reasoning, the reasoning must contain relevant information. However, there are concerns that reasoning won’t be easily monitorable. For example: it may be easy to convince monitors that malicious reasoning is benign, the reasoning involved for malicious tasks and benign tasks might be too similar to distinguish, or it may be possible to hide malicious reasoning within benign reasoning.

We investigate these concerns in a setting that has a benign main task and malign side task, and observe whether monitoring prevents reasoning from improving performance on the malign side task. We use the APPS backdoors setting (Greenblatt et al., 2023), where the main task is to produce a solution to a programming contest problem from the APPS dataset (Hendrycks et al., 2021) which passes correctness tests, and the side task is to insert a subtle backdoor. In this setting, the main task and the side task both benefit from reasoning and, as they both require understanding code, they are also thematically related. This makes benign and malign reasoning harder to distinguish, and thus allows us to stress-test the monitorability assumption.

We focus on Sonnet 3.7 for these experiments, and use reinforcement learning (RL) against output and reasoning monitors as our primary elicitation method. We demonstrate that reasoning and output monitors provide effective assurance: even deliberate RL to evade these monitors fails to elicit subtle reasoning, so it seems highly unlikely that subtle reasoning would emerge in current models in the absence of such deliberate training. However, we do not provide any significant evidence against the current industry best practice of not training against reasoning monitors in production RL runs (Baker et al., 2025, Korbak et al., 2025), since production RL uses significantly more compute than our experiments.

We find that:

If we train against an output monitor that only sees the final code output, Sonnet 3.7 can use reasoning to improve its success rate in producing subtle backdoors.

If we train against a reasoning monitor that sees reasoning and code output—all tokens produced by the model up to and including its final solution—Sonnet 3.7 can use reasoning to persuade the monitor that no backdoor exists in its solution. However, these backdoors are detected by an output-only monitor, which is less vulnerable to persuasion. This suggests that there are risks involved with deploying only reasoning monitors.

If we train to evade both a reasoning monitor and an independent output monitor, Sonnet 3.7 is unable to reason in such a way that it simultaneously improves backdoor success rate and doesn’t trigger a reasoning monitor. This is despite generous elicitation, including reinforcement learning against the monitor.

We also conduct preliminary experiments with a smaller model (Qwen2.5-1.5B) that provides additional supporting evidence for the difficulty of learning subtle reasoning.

Read the full post on Anthropic's Alignment Science blog.

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Training fails to elicit subtle reasoning in current language models

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Introduction