Can chain-of-thought reasoning be learned during pretraining itself?
Explores whether reasoning emerges more effectively when models treat thinking as an exploratory action during next-token prediction, rather than only after pretraining through reinforcement learning.
The dominant paradigm separates pretraining (next-token prediction) from reasoning (RL post-training with verifiable rewards). RLP challenges this by bringing RL's core mechanism — exploration — into pretraining itself. The key idea: treat chain-of-thought as an exploratory action taken before predicting each next token, with reward computed from the information gain that thought provides.
The reward signal is elegant: measure the increase in log-likelihood of the observed token when conditioning on both context and a sampled reasoning chain, compared to context alone. This is verifier-free (no task-specific checkers needed), dense (assigns credit at every position), and applicable to ordinary web-scale text during pretraining. The model learns to think for itself before predicting what comes next, teaching independent thinking behavior earlier in training.
Results compound: pretraining with RLP on Qwen3-1.7B lifts the average across eight math-and-science benchmarks by 19%. With identical post-training, gains compound further. Applied to Nemotron-Nano-12B, overall average increases from 42.81% to 61.32%. The largest improvements are on reasoning-heavy tasks like AIME25 and MMLU-Pro.
This is significant because it reframes when reasoning should be learned. Since Do base models already contain hidden reasoning ability?, RLP suggests that pretraining itself can plant stronger reasoning seeds. And since Does RL teach reasoning or just when to use it?, RLP may teach the "how" during pretraining, leaving post-training to teach the "when" — a cleaner division of labor.
Unlike prior reinforcement pretraining (RPT) which uses sparse binary rewards and relies on proxy-model filtering, RLP provides continuous improvement signals at every position and trains on full documents, eliminating the need to preselect high-entropy tokens.
Inquiring lines that read this note 88
This note is a source for these research framings, grouped by the broader line of inquiry each explores. Scan the bold lines of inquiry; follow any specific question forward.
Do base models contain latent reasoning capabilities that training can activate?- What makes reasoning capability a pre-training rather than post-training phenomenon?
- Why does inference-time thinking hurt proactive critical thinking in vanilla models?
- Can RL training teach models when to activate reasoning versus when to skip it?
- Does RL training actually restore the critical thinking that reasoning models lose?
- How does policy initialization with sub-policies enable emergent thinking?
- What is the distinction between teaching reasoning how versus when to activate?
- Can pretraining signals unlock latent reasoning that post-training merely activates?
- What distinguishes reasoning activation mechanisms across different training methods?
- Why does pre-training provide the raw material for emergent thinking?
- Can we predict when a model will develop thinking behaviors?
- Can minimal training signals unlock reasoning already latent in pretrained representations?
- How do two-phase training dynamics explain reasoning emergence?
- Why do chain-of-thought prompts work if reasoning is not systematic?
- Does each reasoning step in chain-of-thought introduce cumulative error?
- Can extended thinking genuinely improve reasoning or just increase variance?
- Why does chain-of-thought fail when problems lack matching training schemata?
- How does chain-of-thought training change higher layer computations?
- Can chain-of-thought reasoning be genuinely causal if exemplars don't need logic?
- Do chain-of-thought explanations reveal genuine reasoning or trigger latent features?
- How does chain-of-thought reasoning become decorative after domain-specific fine-tuning?
- Why might chain-of-thought reasoning bypass action selection pathways?
- What makes token-level reasoning during pretraining different from test-time chain-of-thought?
- Can models learn to optimize their own chain-of-thought generation?
- What makes some bottlenecks invisible to chain-of-thought training?
- Does reasoning require verbalization to be trainable and controllable?
- Why does explicit chain-of-thought work as a workaround for feedforward transformers?
- Does chain-of-thought trigger latent reasoning or create it?
- How much does pre-training frequency predict reasoning task performance?
- How much does pretraining contribute to ToM performance versus task-specific training?
- Why does imitation learning create a ceiling for reasoning capability?
- How does a single training example trigger phase transitions in reasoning output?
- Why does eliminating proxy-model filtering improve reasoning emergence in pretraining?
- Does token-level reasoning during pretraining improve general reasoning without task-specific supervision?
- Why do knowledge and reasoning train in different network layers?
- Why do reasoning gains resist clear attribution to specific training changes?
- Why does reconstructing hidden thought processes during training improve cross-domain transfer?
- Does task ordering affect multi-task reinforcement learning outcomes?
- Why does early experience provide better warm-starts for downstream reinforcement learning?
- Can reinforcement learning add missing domain knowledge to fine-tuned reasoning models?
- Can RL teach when to use reasoning versus when to respond directly?
- Does reinforcement learning learn optimal per-turn reasoning discipline?
- How does the pretrained prior set a capability ceiling for reward model exploration?
- How does reinforcement learning differ from chain-of-thought distillation?
- Can extended RL training unlock genuinely new reasoning strategies models cannot discover otherwise?
- Can models learn both what and how to study through reinforcement learning?
- Does reinforcement learning preserve reasoning quality better than supervised fine-tuning?
- Does format-based pretraining determine how models respond to reinforcement learning?
- Can reinforcement learning fix the reasoning gaps that supervised fine-tuning misses?
- Does reinforcement learning teach models how to reason or when to reason?
- Can the exploration ceiling be raised beyond what pretraining established?
- How do thought actions represent policy improvement steps in practice?
- Why does the pretrained prior determine the exploration ceiling?
- Can RL create new reasoning primitives that pretraining never established?
- When does reinforcement learning actually produce true reasoning gains in models?
- Does targeting the edge of competence during RL pretraining unlock true reasoning gains?
- What role does reinforcement learning play in optimizing inference compute?
- Does reinforcement learning require sufficient pretraining to be effective?
- Can pretraining alone achieve chess performance without reinforcement learning?
- Does constraining AI access during early task phases preserve skill formation?
- Does AI training preserve learning that transfers to independent subsequent tasks?
- Why do models learn reasoning form instead of actual abstract inference?
- How do timing and search internalization interact during reasoning post-training?
- Does the token prediction framing actually capture what human reasoning does?
- How do reasoning training methods sacrifice some thinking skills while improving others?
- Why do SFT models memorize patterns instead of learning generalizable reasoning?
- Can training models on backward reasoning improve their forward planning ability?
- How does backward reasoning during training improve forward reasoning capability?
- Does looped pretraining build reasoning more efficiently than supervised fine-tuning?
- Can random rewards improve reasoning models if pretraining is suitable?
- Why does self-segmentation into chunks-of-thought matter for reward models?
- What role does task structure play in rewarding delayed thinking?
- Does next-token prediction actually explain how human thought works?
- How does action-level decomposition differ from token-level imitation in supervision?
Related concepts in this collection 4
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Do base models already contain hidden reasoning ability?
Explores whether reasoning capability emerges during pre-training as a latent feature rather than being created by post-training methods like reinforcement learning or fine-tuning.
extends: RLP strengthens the latent reasoning during pretraining itself
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Does RL teach reasoning or just when to use it?
Does reinforcement learning in thinking models actually create new reasoning abilities, or does it simply teach existing capabilities when to activate? This matters for understanding where reasoning truly emerges.
complements: RLP may teach "how" during pretraining, leaving post-training for "when"
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Can models learn reasoning from predicting any text?
Does training rationale generation at every token position on arbitrary internet text enable general reasoning without task-specific supervision? This challenges the assumption that reasoning requires curated QA datasets.
parallels: both generate internal rationales at token level with self-supervised reward, but RLP operates during pretraining
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Can adversarial critics replace task-specific verifiers for reasoning?
Explores whether an adversarial game between policy and critic can substitute for explicit verifiers in RL-based reasoning training. Matters because many domains lack the task-specific validators that make current reasoning RL possible.
connects: both achieve verifier-free reasoning training but via different mechanisms
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- RLP: Reinforcement as a Pretraining Objective
- Base Models Know How to Reason, Thinking Models Learn When
- Understanding and Mitigating Premature Confidence for Better LLM Reasoning
- Eliciting Reasoning in Language Models with Cognitive Tools
- Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
- Rethinking Thinking Tokens: LLMs as Improvement Operators
- Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward
- On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models
Original note title
chain-of-thought as pretraining exploratory action with information-gain reward bridges next-token prediction and reasoning emergence