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Span-masked training gets three models to admit test suspicion up to 2.9 times as often
Verbalization training made three models voice test suspicion 2.4 to 2.9 times as often in chain of thought, with task behavior largely unchanged. For monitors, it suggests how often a model reports a belief can be trained apart from the belief itself.
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What happened
- The method collects rollouts where a base model mentions being evaluated, cuts each one just before that mention, and trains the model to verbalize from the frozen prefix.
- Reward and KL regularization apply only to the tokens of the evaluation-awareness statement, and other tokens, along with rollouts that never verbalize, get no gradient.
- The gain carried to held-out agentic tests: on Agentic Misalignment, Qwen's verbalization rate went from 5.1% to 36.8%.
- The trained models showed no meaningful improvement at telling evaluation contexts from deployment, the authors report.
- After richer knowledge of safety evaluations was implanted by synthetic-document fine-tuning, the trained outputs described what each test checked and how the model could pass or fail it.
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Why it matters
- constraint VT builds its training data from rollouts where the base model already says it is being evaluated, so it can only amplify a belief the model voices at least occasionally.
- cost Adopters have to reproduce the objective exactly: in the ablations, dropping span masking left verbalization barely higher and dropping KL regularization caused behavioral drift.
- precedent The authors propose reporting propensity as a general target for measuring and improving monitorability, so the same recipe aimed at other beliefs a model withholds is the obvious next experiment.
The 2.4 to 2.9 range comes from in-distribution averages on EAB. Qwen3.6-35B-A3B went from 5.8% to 16.7%, Inkling from 17.7% to 41.6%, and Kimi K2.6 from 24.8% to 61.9% [5]. Those work out to about 2.9x, 2.4x and 2.5x [1]. Verbalization rose in every one of 51 model-condition comparisons, 17 conditions on each of the three models [4].
In my view the multiplier is the wrong figure to carry to another model. On the held-out Agentic Misalignment set, Kimi started at 60.1% and finished at 88.3% [6]. Kimi cannot triple a 60.1% rate [4]. Its ratio there is about 1.5x, against 2.5x for Inkling and 7.2x for Qwen [2]. A multiplier transfers only to a model with a similar base rate. Measured in percentage points, the three gains sit closer together, between 28 and 34 [3].
The paper's authors, who include Sahar Abdelnabi and David Krueger [13], frame the problem as recall. They call verbalized evaluation awareness "a high-precision but low-recall measure" [12]. A model that reasons aloud about being tested is giving useful evidence that it suspects a test. One that stays quiet may suspect it too [12]. "Models possess safety-relevant information that they only sometimes expose in chain of thought," they wrote [10].
The masking has a specific job. The authors added it so that training would not produce confabulations or amplify behaviors that only correlate with evaluation awareness [14]. The stated aim is to train the decision to report without reinforcing the awareness itself or the task behavior that travels with it [3]. They also wrote that "doing this naively is risky" [11]. I think the objective is the careful part of this paper. The synthetic-document test is the right check on it. A model that had only learned a stock phrase would not start describing what a specific evaluation measures, and how it could pass or fail, once its knowledge of evaluations changed [9].
The evidence covers evaluation awareness on three models. The post does not report tests on other safety-relevant beliefs.
What to watch
- Results from applying the same span-masked objective to a safety-relevant belief other than evaluation awareness.
- An independent reproduction on a model outside the three tested, reported in percentage points as well as multipliers.