# Controllable Thinking Style A pattern in modern reasoning models where specific marker phrases, injected at a fixed position in the conversation, switch the style of the model's hidden thinking trace (`` block) without changing the answer-generation pipeline. The marker is *trained* — the model has seen training examples where the marker appears at a specific location and the assistant responds with the matching thinking style. At inference time, supplying the marker probabilistically reproduces that style. The clearest documented example is [[deepseek-v4-roleplay-instruct|DeepSeek-V4's roleplay markers]], which toggle between two thinking styles for the same answer task: first-person immersive inner monologue versus third-person scene-and-strategy planning. ## How it differs from prompt engineering Ordinary prompt engineering ("think step by step", "be concise", "play the role of X") works at the level of natural-language instruction. The model interprets the instruction and adjusts as best it can, generalizing from broad training data. A trained style marker works at the level of training-data injection. The model has seen many conversations where exactly this marker, in exactly this position, preceded exactly this output style. Triggering it is closer to invoking a stored mode than to giving an instruction the model has to interpret. The properties that follow: - **Position-sensitive.** The marker only triggers reliably at the position it was injected during training. The DeepSeek guide specifies "end of the first user message"; putting the same text in a system prompt or a later turn loses reliability. - **Wording-sensitive.** Close paraphrases lose reliability. The literal string the model was trained on is what triggers the mode. - **Probabilistic, not enforced.** Unlike a control token (which deterministically gates behavior), a trained marker just biases generation. Re-rolling sometimes helps. - **Persists in conversation history.** Once the marker is in the conversation, the model continues to produce the matching style on later turns without needing the marker re-applied. ## What this implies about training If a model reliably switches thinking style on a specific marker injected at a specific position, the natural explanation is that the training data contained two distinct subsets — one with the marker present at that position and the matching output style, one without. The model learned the conditional. This makes a marker like the DeepSeek roleplay one a **probe of training-data construction**: it reveals that the lab built a dataset of paired (marker, thinking-style) examples for at least two distinct CoT modes. This contrasts with the more common practice (visible in OpenAI o1/o3, Anthropic's extended-thinking modes) of having a single thinking style determined by model variant or by a model-side toggle, with no user-controllable style switch. ## Why labs might build this - **A/B testing in production data.** Two thinking styles can be evaluated empirically on user satisfaction, with the marker as the assignment. - **Domain-specific fine-tuning.** Roleplay, code, math, and analysis benefit from very different thinking-trace styles. Having user-controllable style avoids forcing one mode globally. - **Internal vs external personas.** The character the model is playing wants one style of reasoning; the *meta* layer reasoning about the user wants another. - **Format leakage management.** If the thinking trace is exposed (DeepSeek shows it; OpenAI hides it), letting the user pick the style they want to see matters. ## Why labs might not document it These markers tend to surface organically from community reverse engineering rather than official docs: - Lab doesn't want users to rely on undocumented behavior they may change. - Markers may be a leftover artifact of training-data construction the lab doesn't consider a feature. - The capability competes with the lab's own product surface (Expert Mode toggle, etc.). - Probabilistic behavior is hard to support in an official API contract. ## Adjacent patterns - **Trained system-prompt format.** Most chat models work best with system prompts in a specific shape because that's what they were fine-tuned on. The first-user-position marker is the same idea applied to user messages. - **Anthropic's `` tag prompt.** Asking Claude to use `` tags works because XML-tag-shaped CoT was prevalent in training data. Not the same as a deterministic control token, but the same flavor of "trained-in shape." - **Cycle-conditioning markers in image models.** Stable Diffusion's negative-prompt and quality-tag conventions emerged from training-data structure rather than designed controls. ## Related - [[deepseek-v4-roleplay-instruct]] — the documented case study - [[deepseek]] — the lab and model family