Does this reasoning steering method work consistently across all model sizes?

This reads the question as being about activation-steering methods — interventions that change how a model reasons by adjusting its internal representations rather than fine-tuning it — and whether that consistency holds across model sizes. The short answer the corpus gives: the two papers that test this head-on report that it does generalize, but they're testing different things, and the broader collection suggests 'works consistently' depends heavily on what you're steering toward.

The strongest evidence for size-robustness comes from compression steering. Can we steer reasoning toward brevity without retraining? finds that reasoning verbosity is a single linear direction in activation space — extract one vector from about 50 paired examples, and you can cut chain-of-thought length by two-thirds while holding accuracy, training-free, and the authors specifically claim it generalizes across model sizes and domains. The fact that brevity is one clean direction is what makes it portable: you're not retraining anything size-specific, you're just pushing along an axis that exists in models of different scales.

The second hit widens the picture in an interesting way. Can we trigger reasoning without explicit chain-of-thought prompts? steers a single sparse-autoencoder feature to trigger reasoning itself — not its verbosity — and shows it matches or beats chain-of-thought prompting across six model families. So 'reasoning' isn't bolted on by training; it's a latent capability you can switch on by steering, and it shows up across families. This dovetails with Does RL post-training create reasoning or just deploy it?, which argues RL post-training teaches models *when* to reason, not *how* — the capability pre-exists as activation vectors before any training. If reasoning lives in latent directions that exist regardless of scale, it makes sense that steering them transfers across sizes.

Here's the thing you might not have known to ask: steering works, but the *thing being steered* may sit on a shaky foundation. Does chain-of-thought reasoning actually generalize beyond training data? shows chain-of-thought degrades predictably outside the training distribution — models imitate the form of reasoning without valid logic. And Can reasoning models actually sustain long-chain reflection? finds frontier models hitting only 20-23% on real backtracking tasks. So a steering vector might reliably make any-sized model *reason more* or *reason shorter*, while the underlying reasoning still collapses on unfamiliar problems. Consistency of the steering mechanism is not the same as consistency of the result.

Finally, not every reasoning intervention in the corpus is an activation-steering one, and that contrast is worth seeing. Do reasoning models switch between ideas too frequently? and Why do reasoning models abandon promising solution paths? steer at the *decoding* level — penalizing thought-switching tokens — rather than the activation level, and also work without fine-tuning. Which sentences actually steer a reasoning trace? locates the leverage points at the sentence level. These are all 'training-free steering,' but they operate in different spaces, and the corpus only makes explicit cross-size claims for the activation-space methods. If your method isn't one of those two, the across-all-sizes evidence here is thinner than it looks.