Can latent thought vectors scale language models beyond parameters?
Explores whether explicit latent thought vectors with dual-rate learning create new scaling dimensions independent of model size. This matters because it suggests alternatives to simply building larger models.
Latent-Thought Language Models (LTMs) propose a different scaling strategy than larger parameters or longer contexts: explicit latent thought vectors that follow a prior model in latent space and guide autoregressive token generation. This creates additional scaling dimensions — higher sample efficiency by increasing training compute per token, with further gains by trading model size for more inference steps.
Architecture. Latent thought vectors represent an abstract representation of the entire sequence, controlling the decoder's generation of each token. Training uses variational Bayes with a dual-rate process: fast learning of local variational parameters for the posterior distribution of latent vectors (adapting quickly to specific inputs) coupled with slow learning of global decoder parameters (gradually accumulating general knowledge).
Cognitive inspiration. The dual-rate scheme parallels established cognitive models:
- Declarative-procedural model (Ullman 2004): latent vectors and local parameters parallel declarative/episodic memory; global decoder parameters parallel procedural memory
- Fast-slow learning (Kumaran et al. 2016): fast episodic learning and slow schematic learning interplay
- Language of thought (Fodor 1975): latent thought vectors as "words" of an internal thought language
Scaling properties. LTMs demonstrate superior sample and parameter efficiency compared to conventional autoregressive models and discrete diffusion models. They significantly outperform on validation perplexity and zero-shot language modeling. Emergent few-shot in-context reasoning capabilities scale with both model size and latent size — providing two independent scaling dimensions.
The connection to existing latent reasoning approaches is important but distinct. Can models reason without generating visible thinking tokens? describes depth-recurrent architectures that iterate in latent space at inference time. LTMs use latent vectors differently — as sequence-level abstractions that guide token generation rather than per-token iterative computation. The dual-rate learning provides a training-time mechanism that depth-recurrence does not.
The Titans parallel is also notable: Can neural memory modules scale language models beyond attention limits? separates fast attention (short-term) from slow memory (long-term). LTMs separate fast local adaptation from slow global learning. Both architectures implement the fast-slow cognitive distinction but at different levels — Titans for memory, LTMs for generation.
Inquiring lines that use this note as a source 53
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- Can steering a single latent feature replicate chain-of-thought performance?
- How do embedding dimension limits constrain what concept models can represent?
- What makes internal embeddings useful as multimodal input for language model training?
- Can autoregressive models be trained to produce more cataphoric text?
- Does scaling model size solve compositional generalization problems?
- Can structured artifact sharing replace direct latent thought communication?
- Can latent recurrence and energy minimization both escape the same computational depth constraints?
- Do representations in models causally influence text generation?
- How does this compare to trained autoencoder approaches for thought sharing?
- How does dual-rate learning separate episodic and procedural memory in neural networks?
- Do latent sequence vectors outperform per-token latent iterative computation for reasoning?
- Can fast-slow separation improve both memory and generation in language models?
- How does LatentQA differ from predefined concept steering like representation engineering?
- Do diffusion language models learn differently than autoregressive models?
- Does the prediction unit shape what language models actually learn?
- Can latent space represent reasoning dimensions that text cannot?
- Can conversational memory store precomputed thoughts instead of raw interaction history?
- Can continuous latent reasoning match discrete chain-of-thought without training modifications?
- How do parallel sampling and sequential depth compare as scaling dimensions?
- Can language models generate plausible latent thoughts without human annotation?
- What non-parametric methods could replace latent factors for inductive learning?
- How do static embeddings and contextualized representations divide semantic labor?
- How does training distribution shape what language models understand best?
- Do base models contain latent reasoning that minimal training can unlock?
- Why do language models fail at iterative numerical optimization despite scale?
- How do encode-decode contractive biases create stable attractors in latent space?
- Why does scaling data and model size improve compositional generalization?
- Do pretrained language models carry reusable computational scaffolding for length handling?
- Why does naive randomness fail to improve stochastic latent reasoning models?
- What are the scaling law differences between vision and language learning?
- Why do language models plateau at constraint satisfaction regardless of scale?
- Can language models execute iterative numerical methods in latent space?
- How much training data is truly necessary to unlock latent model reasoning?
- How do continuous concept tokens compare to latent trajectory sampling?
- Do long-term memory modules outperform consolidation into fast weights?
- Can latent recurrence overcome the trainability costs of depth?
- Can models learn to optimize their own chain-of-thought generation?
- Why should scaling laws be understood as properties of data distribution rather than training in general?
- Why does recursion on latent state drive generalization better than hierarchy?
- Why is latent-level prediction more sample-efficient than token-level prediction?
- How can language models extract more value from fewer demonstrations?
- What is the comprehension-generation asymmetry in language models?
- What makes looped latent computation more efficient than scaling attention capacity?
- Can minimal training signals unlock latent reasoning capability in base models?
- Do language models need words to think or just latent structure?
- What power-law scaling patterns emerge when consistency models are trained at scale?
- Why do optimal learning dynamics improve scaling law coefficients specifically?
- What empirical evidence supports the Learning Law on real language models?
- How do latents at the same hierarchy level become more correlated than tokens?
- What prevents representation collapse in latent-prediction world models like JEPA?
- What latent reasoning capability do base models already possess before training?
- What are the concrete efficiency gains of linear-attention state-space models?
- Can fixed-size latent states losslessly store arbitrary input context?
Related concepts in this collection 4
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Can models reason without generating visible thinking tokens?
Explores whether intermediate reasoning must be verbalized as text tokens, or if models can think in hidden continuous space. Challenges a foundational assumption about how language models scale their reasoning capabilities.
different latent approach: per-token iterative computation vs sequence-level latent vectors
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Can neural memory modules scale language models beyond attention limits?
Can separating short-term attention from adaptive long-term memory allow models to efficiently handle context windows exceeding 2M tokens while maintaining competitive performance?
Titans implements fast-slow at memory level; LTMs implement it at generation level
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Can inference compute replace scaling up model size?
Explores whether smaller models given more thinking time during inference can match larger models. Matters because it reshapes deployment economics and compute allocation strategies.
LTMs demonstrate this: model size can be traded for inference steps
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Can computational power accelerate scientific discovery itself?
Does the pace of research breakthroughs scale with computing resources, like model performance does? ASI-ARCH tested this by running thousands of autonomous experiments to discover neural architectures.
LTMs provide new dimensions for architecture search
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Scalable Language Models with Posterior Inference of Latent Thought Vectors
- Reasoning to Learn from Latent Thoughts
- Bigger is not always better: The importance of human-scale language modeling for psycholinguistics
- Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
- The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs
- Learn from your own latents and not from tokens: A sample-complexity theory
- Scaling Laws for Neural Language Models
- Farther the Shift, Sparser the Representation: Analyzing OOD Mechanisms in LLMs
Original note title
latent-thought language models introduce additional scaling dimensions beyond parameters by incorporating explicit latent thought vectors with dual-rate learning