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Do base models contain latent reasoning capabilities that training can unlock?
A broader line of inquiry — a family of 46 specific questions the research asks around this. Follow one into its inquiring-line page, or move sideways to a related line below.
Questions in this line of inquiry 46
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- Do base models contain latent reasoning that minimal training can unlock?
- Can minimal training signals unlock latent reasoning capability in base models?
- Does the base model already contain latent reasoning capability?
- Can latent reasoning scale test-time compute without verbalized tokens or special training?
- Why might latent reasoning capture types of thinking that verbalized CoT cannot?
- Do base models truly possess latent reasoning capability?
- Can models possess latent reasoning capability that training signals fail to unlock?
- What mechanisms activate latent reasoning capabilities already present in base models?
- Can latent reasoning in continuous space scale beyond supervised reasoning tasks?
- Can latent reasoning scale test-time compute without verbal tokens?
- Can continuous latent reasoning match discrete chain-of-thought without training modifications?
- Can reasoning happen in latent space without chain of thought?
- Does latent reasoning capability exist in base models before any training?
- Can minimal training signals unlock reasoning already latent in pretrained representations?
- What latent reasoning capability do base models already possess before training?
- Can latent reasoning achieve the same substitution without tokens?
- Does chain-of-thought trigger latent reasoning or create it?
- Why does latent reasoning override no-think instructions in models?
- How do compact latent dynamics enable planning without explicit chain of thought?
- What other triggers can activate the latent reasoning capability?
- Can latent reasoning stay readable without explicit token-by-token decoding?
- Why does latent-level prediction beat token-level prediction for reasoning?
- Can models reason at inference without specialized internal training?
- Does the latent-explicit gap widen beyond 3B parameters on reasoning tasks?
- Can models hide their reasoning in continuous space rather than natural language?
- Can latent reasoning architectures work as retrofits to existing models?
- Can latent space represent reasoning dimensions that text cannot?
- How do depth and length constraints emerge in latent reasoning without explicit training?
- How much training data is truly necessary to unlock latent model reasoning?
- What makes some reasoning strategies genuinely novel versus latent?
- Can structured workflows unlock latent reasoning abilities that raw models don't show?
- Can articulating latent reasoning processes improve transfer across domains?
- Can language models generate plausible latent thoughts without human annotation?
- Can we predict when a model will develop thinking behaviors?
- Does latent manipulation outperform token-level prediction for efficiency?
- How much explicit verbal signal must latent chains retain to perform well?
- What makes thought identifiability provable without auxiliary training data?
- Do base models already contain latent behavioral principles waiting to be amplified?
- Can latent reasoning mechanisms and recursive tracking mechanisms be combined effectively?
- What affordances do normalizing flows add over opaque vector reasoning?
- How does soft thinking compare to sampling multiple independent reasoning paths?
- What other latent LLM capabilities remain inactive without explicit activation cuing?
- How does soft thinking achieve stochastic exploration without explicit training?
- Do extended thinking blocks access latent empathetic capabilities in models?
- Why does naive randomness fail to improve stochastic latent reasoning models?
- How much reasoning catalyst data is actually needed for improvement?