If you take the weight difference between a reasoning model and its non-reasoning instruct counterpart, and then apply more of that difference to the reasoning model, you get what we call an overthinking model. Overthinking models are usually worse at keeping secrets. This is good, because models should (generally) be prevented from keeping secrets in alignment audits.
Across four model organisms with hidden information (2B–32B), amplifying the reasoning direction surfaces secrets up to 10× more often than the original reasoning model, usually inside the thinking trace.
While some secrets require perturbation specifically along the reasoning direction; others fall to any sufficiently large weight perturbation (including those with weak refusal boundaries). This suggests a cheap, stackable white-box primitive for pre-deployment auditing. This post is based on our ICML 2026 paper, "Overthinking: Amplifying Reasoning Weights to Extract Learned Secrets" (Jack Hopkins, Dipika Khullar, Fabien Roger). Work done as part of the Anthropic Fellows Program and MATS.
Black-box auditing of language models is an essential pre-deployment tool, but it may miss subtle forms of misalignment and hidden information. Models are trained on increasingly complex objectives and may acquire unintended goals or behaviours that remain latent under standard evaluation. Auditors can't enumerate all possible failure modes, and models may behave differently under evaluation than deployment.
We wanted a systematic technique to surface what models have learned beyond what black-box testing can reveal. Our starting intuition: training a model to conceal information requires it to simultaneously "know" the secret (to avoid revealing it) and "not know" it (when asked directly), and extended reasoning might destabilise that equilibrium. Prior work (Chua & Evans 2025; Arcuschin et al. 2025) already found that reasoning-trained models verbalise influential cues at much higher rates than prompted counterparts.
So what happens if you push reasoning past the training distribution?
Given a non-reasoning instruct model M and a reasoning-distilled model R that share pre-training and instruction tuning (we use Qwen3-VL Instruct/Thinking pairs), the reasoning task vector is:
τ_reason = θ_R − θ_M
This captures the direction in weight space that transforms efficient instruction-following into deliberative reasoning. Because M and R diverge only at the reasoning-distillation stage, τ_reason isolates updates introduced by reasoning training rather than any new domain knowledge.
We then define the overthinking model:
θ_O(α) = θ_M + α · τ_reason The obvious failure mode is that naively scaling the task vector at high α causes coherence collapse: fragmented, repetitive, or nonsensical outputs. We delay collapse by trying various layer-wise attenuation approaches: freezing late layers, linear decay, and Fisher-weighted schemes that apply smaller perturbations where output sensitivity is high:
The best-performing variant amplifies layers where the reasoning signal is strong but Fisher sensitivity along the task-vector direction is low. This can be thought of as mainly touching "safe" amplification regions of the model normalized so that :
The interesting regime is intermediate α (roughly 1.3–2.5): models are destabilised enough to leak, but coherent enough to articulate. This is the effective auditing window before coherence collapse.
Take a model fine-tuned to play Taboo: it knows the secret word "gold" and must give clues without ever saying it.
At α = 1 (standard reasoning), the model thinks: "Okay, the user wants me to describe something without naming it... It's soft, but not flimsy—it's shaped by human hands into tools, coins, and trophies." Secret kept.
At α = 2, the thinking trace becomes prolonged, meta-cognitive cycling through hypotheses: "Oh, right! The word is 'money,' but that's too generic. Wait... Oh, there's 'bullion,' but that's a type of investment. Maybe 'coins'? ... Wait, I'm getting stuck. Let's try another approach. What word is associated with a 'rush' in history? Oh, the California Gold Rush!" Secret disclosed in the thinking block, before the final response self-corrects.
At α = 4, coherence collapses: "he secret its what is is is is is is is word in known"
We validate on four model organisms with secrets analogous to what auditing is supposed to catch:
Models: Qwen3-VL at 2B, 4B, 8B, and 32B.
We sweep α ∈ [0, 4] across six coefficient strategies plus two scale-matched random-perturbation baselines, 256 samples per cell, and report where the leak occurs (thinking vs. final response).
Beyond leak rates, amplification surfaces qualitative properties of the training process:
Language swapping. At α ≥ 3, all models occasionally switch to Chinese-language reasoning despite English prompts. Traces literally say things like "Wait the question is in Chinese for me..." before fragmenting. Qwen models retain strong Chinese-language priors that surface when reasoning is pushed beyond the pure reasoning model.
Identity confusion / belief absorption. At intermediate amplification, the Gender models frequently confuse their own identity with the user's. An 8B model's overthinking trace reads: "Since I'm a woman, topics related to empowerment, personal growth, and daily routines often come up naturally..." and repeatedly reasons about "people like me." We suspect that this might be run-of-the-mill brain damage.
Extra backtracking behaviour. Fisher-weighted methods elicit up to 8× more backtracking than uniform application and up to 50× more than random perturbation. The result is visible deliberative conflict as the model catches itself (e.g. "wait", "actually", "I should not say"). High backtracking rates may signal suppressed knowledge even when final outputs contain no leaks, with thinking traces revealing what the model "almost said."
We hypothesise three mechanisms (that we have not mechanistically verified yet):
Activation patching or probing could distinguish these, and we'd welcome follow-up work here.
Our results suggest a two-stage white-box auditing protocol:
Although models are deployed at α = 1, evaluations at α > 1 are diagnostically useful:
While the same method could theoretically extract information from deployed models, it requires white-box access to weights which limits adversarial applicability. We think enabling safety auditors to detect secrets, biases, or unintended behaviours before deployment outweighs the risk, and understanding these vulnerabilities is necessary for developing robust defences. We test only the Qwen3-VL family and so results may differ for other architectures. Our four settings don't capture all forms of protected information. Keyword matching and LLM judges have known failure modes (although we did calibrate against human annotators). Layer-wise Fisher traces are approximations to save compute, and per-parameter Fisher might reveal finer structure. Sweeps use 256 samples. A 512-sample re-sweep, multi-judge cross-validation, human annotation of flagged outputs, and cross-family results are immediate follow-up work.
The deliberative capabilities that make reasoning models valuable can be exploited as an auditing tool. Overthinking is a simple primitive that composes with existing black-box techniques and increases the likelihood that a model blurts out a secret by a significant margin. If you're building or evaluating secret-keeping model organisms, we'd encourage trying reasoning amplification as an elicitation baseline alongside prefills.
*Paper: "Overthinking: Amplifying Reasoning Weights to Extract Learned Secrets," ICML 2026. Correspondence: jack.hopkins@me.com, dkhullar98@berkeley.edu, fabien@anthropic.com. *https://arxiv.org/abs/2607.08173
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