opendatalab / meta-rater-1b-reasoning

PRRC-Reasoning Language Model (1.3B Parameters, 30B Tokens)

This repository contains the model described in the paper Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models .

Code: https://github.com/opendatalab/Meta-rater

Model Description

This is a 1.3B parameter transformer-based decoder-only language model trained from scratch on 30B tokens selected from SlimPajama dataset using the Reasoning dimension of the PRRC framework. The training data was curated by selecting text with high reasoning complexity, focusing on content that requires multi-step logical analysis and critical thinking.

Model Details

• Architecture : Transformer decoder-only
• Parameters : 1.345B (1,345,423,360 parameters)
• Training Tokens : 30B tokens
• Context Window : 1,024 tokens
• Vocabulary Size : 32,000 (LLaMA tokenizer)
• Data Selection Method : Top-k selection based on Reasoning scores
• Rating Model : ModernBERT-base fine-tuned for Reasoning assessment

Architecture Specifications

• Hidden Dimension : 2,048
• Number of Layers : 24
• Attention Heads : 16
• Key-Value Heads : 16
• MLP Ratio : 8/3
• Position Encoding : RoPE (base=10,000)

Data Selection Criteria

The training data was selected using the Reasoning rating model, which evaluates:

• Logical Structure : Multi-step reasoning and argument chains
• Analytical Depth : Complex analysis and critical evaluation
• Causal Relationships : Identification and exploration of cause-effect patterns
• Problem Solving : Strategic thinking and solution development
• Evidence Integration : Synthesis of multiple information sources

Selected texts typically include:

• Analytical essays and research papers
• Problem-solving discussions and case studies
• Philosophical and scientific arguments
• Strategic planning documents
• Complex technical analyses

Training Details

• Hardware : 32x NVIDIA A800 GPUs
• Global Batch Size : 4,194,304 tokens
• Learning Rate : 5e-5
• Optimizer : Adam (β₁=0.9, β₂=0.95, ε=1e-8)
• Training Time : ~14 hours

Performance Results

Downstream Task Performance (Average Accuracy)

• General Knowledge : 55.57% (+2.78% vs Random)

  • ARC-Easy: 55.35%
  • ARC-Challenge: 27.05%
  • SciQ: 84.30%

• Commonsense Reasoning : 44.86% (+0.92% vs Random)

  • HellaSwag: 41.34%
  • SIQA: 40.36%
  • WinoGrande: 52.87%

• Reading Comprehension : 30.48% (+0.46% vs Random)

  • RACE: 30.95%
  • OpenbookQA: 30.00%

• Overall Average : 45.28% (+1.50% vs Random)

Key Findings

• Reasoning Enhancement : Improved logical thinking and analysis capabilities
• Problem Solving : Enhanced ability to work through complex problems
• Knowledge Application : Better at applying knowledge to new situations
• Analytical Skills : Stronger performance in tasks requiring multi-step reasoning

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model_name = "opendatalab/meta-rater-1b-reasoning"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text (particularly good for analytical content)
prompt = "To solve this problem, we need to consider several factors:"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        inputs.input_ids,
        max_length=100,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Applications

This model is particularly well-suited for:

• Analytical writing and problem-solving tasks
• Educational content focused on critical thinking
• Research assistance and hypothesis development
• Strategic planning and decision-making support
• Complex reasoning tasks and logic puzzles
• Academic writing requiring argumentation
• Case study analysis and evaluation

Strengths

• Enhanced logical reasoning and analytical capabilities
• Improved problem-solving approach and methodology
• Better at handling complex, multi-step arguments
• Strong performance on knowledge-intensive reasoning tasks
• Effective at synthesizing information from multiple sources
• Good at identifying causal relationships and patterns

Limitations

• May generate overly complex reasoning for simple questions
• Could prioritize analytical depth over accessibility
• Limited context window (1,024 tokens)
• No instruction tuning or safety alignment
• May struggle with creative or intuitive tasks

Reasoning Capabilities

This model demonstrates enhanced abilities in:

• Deductive Reasoning : Drawing logical conclusions from premises
• Inductive Reasoning : Identifying patterns and generalizations
• Causal Analysis : Understanding cause-and-effect relationships
• Problem Decomposition : Breaking complex problems into manageable parts
• Evidence Evaluation : Assessing the strength and relevance of information
• Hypothesis Formation : Developing testable explanations

Comparison with Baselines

• vs Random Baseline : +1.50% overall, with consistent improvements across categories
• vs Other PRRC Dimensions : Competitive performance with focus on analytical tasks
• vs Meta-rater All (25) : Shows specialized improvement in reasoning-heavy applications

Citation

If you use this model in your research, please cite:

@article{zhuang2025meta,
  title={Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models},
  author={Zhuang, Xinlin and Peng, Jiahui and Ma, Ren and Wang, Yinfan and Bai, Tianyi and Wei, Xingjian and Qiu, Jiantao and Zhang, Chi and Qian, Ying and He, Conghui},
  journal={arXiv preprint arXiv:2504.14194},
  year={2025}
}

License

Please refer to the license terms of the original SlimPajama dataset and follow applicable data licensing requirements.

Contact

For questions or issues, please contact the authors or open an issue in the repository.