jepa_llm_prototypes

JEPA-Style LLM Prototypes

Making decoder-only transformers predict state consequences instead of tokens.

What's This?

Three approaches to convert a standard LLM into a world model that predicts "what happens next" given a state and action — like JEPA but for language models.

Files

Quick Start

1. Open any notebook in Google Colab
2. Set runtime to GPU (Runtime → Change runtime type → H100)
3. Run all cells
4. Watch the model learn to predict state transitions

The Core Idea

Normal LLM:     tokens → transformer → next token
JEPA-style:     (state, action) → transformer → next state embedding

Instead of predicting words, the model predicts what the world looks like after an action.

Three Approaches

Option 1: Sentence Encoder (Simplest)

• Uses all-MiniLM-L6-v2 for embeddings
• Trains only a small predictor network
• Best for: quick testing, limited GPU

Option 2: LLM Hidden States (Medium)

• Uses GPT-2's internal representations
• Trains projection + predictor heads
• Best for: better accuracy, still fast

Option 3: Autoencoder (Most Powerful)

• Learns domain-specific state embeddings
• Trains encoder + decoder + predictor
• Best for: production, domain adaptation

Example

# Input
state = "Document is in draft status with 2 sections"
action = "User submits for review"

# Model predicts
next_state = "Document is pending review"  # via embedding similarity

Requirements

• Python 3.8+
• PyTorch
• Transformers
• Sentence-Transformers (Option 1)
• GPU recommended (runs on CPU but slow)

All dependencies install automatically in the notebooks.

Next Steps

• Swap synthetic data for real enterprise workflow logs
• Scale up base model (Llama, Qwen, Palmyra)
• Add multi-step trajectory prediction
• Integrate with planning/search algorithms

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Experimental code — have fun breaking it.

Coauthors: Writer Agent & OpenCode