CoLaR (Compressed Latent Reasoning) is a latent reasoning model based on LLaMA that uses a specialized LatentHead module for generating continuous latent representations. This model is part of the Parallel Test-Time Scaling for Latent Reasoning Models framework.

Model Details

Base Architecture: LLaMA Language Model
Model Class: ColarLlama (extends LlamaForCausalLM)
Special Features: LatentHead module for latent space generation
Latent Tokens: Uses special token <|latent|> for latent reasoning
End Token: Uses ### as the end-of-latent marker
Input Format: Direct input format with latent tokens

Related Models

This repository includes other latent reasoning models that you might find useful:

ModalityDance/latent-tts

Installation

Download the model from HuggingFace:

huggingface-cli download dd101bb/latent-tts-colar --local-dir checkpoints/colar

Quick Start

Basic Usage

python

import torch
from transformers import AutoTokenizer
from src.generation_mixin import LatentGenerationMixin, LatentGenerationConfig
from src.paths import MODELS

# Load tokenizer
model_id = "checkpoints/colar"
tokenizer = AutoTokenizer.from_pretrained(model_id)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

# Get latent token IDs
latent_id = tokenizer.convert_tokens_to_ids("<|latent|>")
end_id = tokenizer.convert_tokens_to_ids("###")

# Create model class with generation mixin
class LatentCoLaR(MODELS["colar"]["class"], LatentGenerationMixin):
    pass

# Load model
model = LatentCoLaR.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,  # Recommended for LLaMA models
)

# Prepare input
question = "What is 2 + 2?<|latent|>"
inputs = tokenizer(question, return_tensors="pt").to(model.device)

# Configure generation
generation_config = LatentGenerationConfig(
    max_new_tokens=128,
    max_latent_length=64,  # CoLaR uses max_latent_length instead of latent_length
    latent_do_sample=True,
    latent_do_sample_by="dropout",  # or "noise"
    dropout_p=0.1,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
)

# Generate
output = model.generate(
    **inputs,
    generation_config=generation_config,
    num_return_sequences=1,
)

# Decode result
result = tokenizer.decode(output[0], skip_special_tokens=True)
print(result)

Batch Processing

The model fully supports batch processing with Transformers:

python

import torch

# Prepare batch inputs
questions = [
    "What is 2 + 2?<|latent|>",
    "What is 5 * 3?<|latent|>",
    "What is 10 - 4?<|latent|>",
]
inputs = tokenizer(questions, return_tensors="pt", padding=True).to(model.device)

# Generate for batch
outputs = model.generate(
    **inputs,
    generation_config=generation_config,
    num_return_sequences=1,
)

# Decode batch results
results = tokenizer.batch_decode(outputs, skip_special_tokens=True)
for result in results:
    print(result)

Model Architecture

LatentHead Module

CoLaR uses a specialized LatentHead for generating latent representations:

python

class LatentHead(nn.Module):
    def __init__(self, feature_size, intermediate_size=512):
        super().__init__()
        self.fc = nn.Sequential(
            nn.Linear(feature_size, intermediate_size),
            nn.GELU(),
            nn.Linear(intermediate_size, intermediate_size),
            nn.LayerNorm(intermediate_size),
        )
        self.mean = nn.Linear(intermediate_size, feature_size)

The latent embeddings are scaled by latent_embedding_std (default: 0.018 for LLaMA-3.2 models).

Generation Parameters

LatentGenerationConfig

max_new_tokens (int): Maximum number of tokens to generate
max_latent_length (int): Maximum number of latent tokens (default: 64)
latent_do_sample (bool): Whether to use stochastic sampling
latent_do_sample_by (str): Sampling method - "dropout" or "noise"
dropout_p (float): Dropout probability for Monte Carlo Dropout (e.g., 0.1)
noise_std (float): Standard deviation for Additive Gaussian Noise

Sampling Methods

Monte Carlo Dropout: Randomly drops activations during forward passes

python
1generation_config = LatentGenerationConfig( 2 latent_do_sample_by="dropout", 3 dropout_p=0.1, 4 # ... 5)
Additive Gaussian Noise: Injects noise into latent embeddings

python
1generation_config = LatentGenerationConfig( 2 latent_do_sample_by="noise", 3 noise_std=0.1, 4 # ... 5)

Answer Extraction

CoLaR uses a special answer format with "Answer:" prefix:

python

from src.paths import colar_extract_answer_number

# Extract answer from generated text
answer = colar_extract_answer_number(result)
print(f"Answer: {answer}")

Evaluation

Run evaluation using the provided scripts:

bash

# For CoLaR (LLaMA based models)
./run_tests_llama.sh

Model Card

Paper: Parallel Test-Time Scaling for Latent Reasoning Models
HuggingFace: dd101bb/latent-tts-colar
Benchmarks: GSM8K Test, GSM8K Hard, MultiArith

Notes

Data Type: Recommended to use torch.bfloat16 or torch.float16 for LLaMA models
Memory: LLaMA models typically require more GPU memory than GPT-2 models
Latent Length: CoLaR uses max_latent_length instead of fixed latent_length

Citation

If you use this model, please cite:

bibtex

@misc{you2025paralleltesttimescalinglatent,
      title={Parallel Test-Time Scaling for Latent Reasoning Models}, 
      author={Runyang You and Yongqi Li and Meng Liu and Wenjie Wang and Liqiang Nie and Wenjie Li},
      year={2025},
      eprint={2510.07745},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.07745}, 
}

@misc{tan2025thinksilentlythinkfast,
      title={Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains}, 
      author={Wenhui Tan and Jiaze Li and Jianzhong Ju and Zhenbo Luo and Jian Luan and Ruihua Song},
      year={2025},
      eprint={2505.16552},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.16552}, 
}