Does token-level reasoning during pretraining improve general reasoning without task-specific supervision?
This explores whether teaching a model to reason at the level of individual tokens during pretraining — instead of fine-tuning it on labeled reasoning tasks afterward — actually produces broad, general reasoning ability.
This explores whether reasoning can be planted into a model during pretraining itself, at the granularity of individual tokens and without anyone hand-labeling reasoning problems — and whether that yields general capability rather than narrow tricks. The short answer from the corpus: yes, and three different methods converge on it from different angles. Quiet-STaR trains a model to generate a private rationale at *every* token position on ordinary internet text, judging each rationale by whether it improves the prediction of what comes next — reasoning emerges as a side effect of better language modeling, with no reasoning dataset in sight Can models learn reasoning from predicting any text?. Reinforcement Pre-Training reframes plain next-token prediction as a reasoning task and draws its reward straight from the corpus, so the text itself is the verifier Can next-token prediction become a reasoning task with RL?. And RLP treats chain-of-thought as an exploratory action during pretraining, rewarding it by how much the 'thinking' raises the log-likelihood of the next tokens — a verifier-free signal that lifted math and science benchmarks by ~19% Can chain-of-thought reasoning be learned during pretraining itself?. The shared trick across all three is that the corpus grades itself, which is what frees them from task-specific supervision.
Why should reasoning be *learnable* this early, rather than something you bolt on at fine-tuning time? Two notes suggest the capacity is already latent in the base model. One finds that five independent techniques — RL steering, critique fine-tuning, decoding tweaks, SAE feature steering, RLVR — all surface reasoning that was already sitting in base-model activations, implying post-training *selects* reasoning rather than creating it Do base models already contain hidden reasoning ability?. Another, analyzing five million pretraining documents, shows reasoning leans on broad, transferable *procedural* knowledge spread across many sources, unlike fact recall which depends on narrow memorization of specific documents Does procedural knowledge drive reasoning more than factual retrieval?. Put together, this reframes the whole question: token-level pretraining works not because it teaches reasoning from scratch but because reasoning is a diffuse, procedural property of the pretraining corpus that the right objective can amplify.
There's also a clue about *where* the signal lives. Only about 20% of tokens — the high-entropy 'forking points' where the model genuinely decides what to do next — carry the reasoning signal; training on just those matches full-gradient updates Do high-entropy tokens drive reasoning model improvements?. That dovetails neatly with token-level pretraining: if a minority of decision-tokens does the work, inserting reasoning at the token level is targeting exactly the places that matter.
The corpus also plants two flags worth knowing about before you get too optimistic. First, what these methods install may be the *form* of reasoning more than valid logic — models trained on deliberately corrupted, irrelevant reasoning traces perform comparably to those trained on correct ones, suggesting traces act as computational scaffolding rather than meaningful steps Do reasoning traces need to be semantically correct?. Second, chain-of-thought generalization is distribution-bounded: it degrades predictably once the task, length, or format drifts from training, producing fluent but logically inconsistent output Does chain-of-thought reasoning actually generalize beyond training data?. So 'improves general reasoning' is real on-benchmark, but the generality has measured limits.
The thing you may not have known you wanted to know: the most promising route to reasoning models might run *backward* from the usual recipe. Instead of pretraining for language and then fine-tuning for reasoning on curated problems, these papers suggest you can make the pretraining objective itself reasoning-shaped — and because the corpus supplies its own reward, you sidestep both the cost of labeled data and the reward-hacking that plagues supervised reward models.
Sources 8 notes
Quiet-STaR trains language models to generate rationales at every token position during pretraining on arbitrary internet text, enabling general reasoning without task-specific datasets. Rationale quality is judged by predictive accuracy rather than labeled correctness, allowing reasoning competence to emerge as a side effect of improved language modeling.
Reinforcement Pre-Training transforms next-token prediction into a reasoning task by providing verifiable rewards from the corpus itself, eliminating reward hacking and enabling inference-time scaling during pretraining. This suggests token-level reasoning patterns during pretraining strengthen downstream RL fine-tuning.
RLP treats CoT as exploratory action during pretraining, using log-likelihood improvement as verifier-free reward. Applied to Qwen3-1.7B and Nemotron-Nano-12B, the method improves math and science benchmarks substantially, suggesting reasoning can be planted earlier in training.
Five independent mechanisms—RL steering, critique fine-tuning, decoding changes, SAE feature steering, and RLVR—all elicit reasoning already present in base model activations. Post-training selects rather than creates reasoning; the bottleneck is elicitation, not capability acquisition.
Analysis of 5 million pretraining documents shows reasoning relies on broad, transferable procedural knowledge from diverse sources, unlike factual recall which depends on narrow, document-specific memorization of target facts.
Only ~20% of tokens exhibit high entropy as pivotal reasoning decision points; RLVR primarily adjusts these forking tokens. Training exclusively on them matches or exceeds full-gradient performance, revealing that the minority carries the learning signal.
Models trained on systematically irrelevant traces maintain solution accuracy and sometimes improve out-of-distribution generalization, suggesting traces function as computational scaffolding rather than meaningful reasoning steps.
DataAlchemy experiments show CoT fails systematically under distributional shifts in task, length, and format. Models produce fluent but logically inconsistent reasoning — imitating reasoning form without valid underlying logic.