Training LLM Reasoners: Exploring Compute-Optimal Sampling from Weaker Language Models

Основные понятия

Training language models for reasoning tasks by sampling synthetic data from smaller, weaker language models (instead of larger, more expensive ones) proves to be more compute-optimal, leading to improved performance and generalization capabilities.

Аннотация

• Bibliographic Information: Bansal, H., Hosseini, A., Agarwal, R., Tran, V. Q., & Kazemi, M. (2024). Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling. arXiv preprint arXiv:2408.16737v2.

• Research Objective: This paper investigates the compute-optimality of using weaker but cheaper language models (WC) versus stronger but more expensive models (SE) for generating synthetic training data to improve the reasoning capabilities of large language models (LLMs).

• Methodology: The researchers compare WC and SE models in terms of data quality (coverage, diversity, and false positive rate) and downstream task performance after fine-tuning on synthetic data. They experiment with different fine-tuning setups: knowledge distillation, self-improvement, and a novel weak-to-strong improvement paradigm. Experiments are conducted on the MATH and GSM-8K reasoning datasets using Gemma2 and Gemini 1.5 language model families.

• Key Findings:

  • WC models generate data with higher coverage and diversity than SE models at the same computational budget.
  • Despite a higher false positive rate in the generated data, LLMs fine-tuned on WC-generated data consistently outperform those trained on SE-generated data across multiple benchmarks and fine-tuning setups.
  • This observation holds true even when scaling to state-of-the-art models like Gemini 1.5, where the weaker "Flash" model yields better training data than the stronger "Pro" model.

• Main Conclusions:

  • The prevailing practice of relying on SE models for synthetic data generation might not be the most compute-optimal approach.
  • Sampling from WC models offers a more efficient way to train advanced LLM reasoners, especially as the performance gap between small and large LLMs continues to narrow.

• Significance: This research provides a novel perspective on optimizing resource allocation for training LLMs, potentially leading to more efficient and accessible development of advanced language models.

• Limitations and Future Research:

  • The study primarily focuses on reasoning tasks and two specific language model families. Further investigation is needed to assess the generalizability of these findings across other tasks and model architectures.
  • Future research could explore optimal strategies for mixing data from WC and SE models to further enhance performance.

Статистика

Gemma2-9B achieves 11% higher coverage and 86% higher diversity on the MATH dataset compared to Gemma2-27B at the same computational budget.
Finetuning Gemma-7B on WC-generated data leads to relative gains of up to 6% and 5.8% at low and high sampling budgets, respectively, compared to using SE-generated data.
Training Gemma2-27B with data from Gemma2-9B (W2S-I) outperforms training with self-generated data, achieving relative gains of 5.8% and 4.3% at low and high sampling budgets, respectively.
Gemini-1.5-Flash generates data with a coverage of 81% compared to 61.1% for Gemini-1.5-Pro at the same cost.
Finetuning Gemma models on Flash-generated data outperforms using Pro-generated data, with relative gains of 31.6%, 14.4%, and 10.9% for Gemma-7B, Gemma2-9B, and Gemma2-27B, respectively.

Цитаты

"Our findings reveal that models finetuned on WC-generated data consistently outperform those trained on SE-generated data across multiple benchmarks and multiple choices of WC and SE models."
"These results challenge the prevailing practice of relying on SE models for synthetic data generation, suggesting that WC may be the compute-optimal approach for training advanced LM reasoners."
"Our results indicate that it is more compute-optimal to sample from a WC model as opposed to the common-practice of sampling from a SE model."

Ключевые выводы из

Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling

by Hritik Bansa... в arxiv.org 10-10-2024

https://arxiv.org/pdf/2408.16737.pdf

Дополнительные вопросы

How might these findings on compute-optimal sampling influence the development and accessibility of LLMs, particularly for researchers and developers with limited computational resources?

This research could democratize access to high-performing LLMs, particularly for those with limited resources. Here's how:

Reduced reliance on massive computational power: The study demonstrates that generating synthetic training data from weaker, less expensive models (WC - Weaker and Cheaper) can be more efficient than using larger, more computationally demanding models (SE - Stronger and Expensive). This is a significant finding as it reduces the barrier to entry for researchers and developers who may not have access to vast computing clusters.
Faster experimentation and iteration: Training and experimenting with smaller models is inherently faster. This allows for quicker iteration on ideas and exploration of different approaches, potentially leading to faster progress in LLM research and development.
Focus on novel training methodologies: With compute power becoming less of a bottleneck, the emphasis can shift towards developing more innovative and efficient training methodologies, such as the Weak-to-Strong Improvement (W2S-I) paradigm highlighted in the paper.
Potential for specialized LLMs: The ability to train effective LLMs with less data and compute opens up the possibility of developing more specialized models tailored for specific tasks or domains, even with limited resources.
However, it's important to note that while this research is promising, challenges remain in terms of ensuring the quality and reliability of models trained with WC-generated data.

Could the higher false positive rate in WC-generated data lead to unexpected biases or limitations in the reasoning capabilities of the fine-tuned models, despite their superior performance on benchmark datasets?

Yes, the higher false positive rate (FPR) in WC-generated data is a valid concern and could lead to several issues:

Hidden biases and limitations: While the fine-tuned models show strong performance on benchmark datasets, the higher FPR suggests they might be learning to exploit subtle biases or patterns in the WC-generated data that don't generalize well to real-world scenarios. This could result in unexpected errors or limitations in their reasoning capabilities when deployed in complex, less predictable environments.
Overfitting to incorrect reasoning paths: Even though the final answers might be correct, learning from data with a higher proportion of incorrect reasoning chains could lead to models developing a skewed understanding of the underlying reasoning process. This could hinder their ability to learn more robust and generalizable reasoning skills.
Difficulty in identifying and mitigating biases: The nature of these biases might be harder to identify and mitigate compared to biases stemming from human-annotated data. This is because the biases are implicitly encoded in the reasoning patterns of the WC model itself, making them less transparent.
Addressing this FPR issue is crucial. Potential solutions could involve:

Improved filtering mechanisms: Developing more sophisticated methods for filtering out incorrect reasoning chains from the WC-generated data, potentially leveraging stronger models or human-in-the-loop approaches.
Curriculum learning: Gradually increasing the difficulty of the training data by incorporating more data from stronger models as training progresses.
Robustness training: Incorporating techniques that encourage models to be less sensitive to noise and inconsistencies in the training data, improving their ability to generalize.

If weaker models can effectively train stronger models, does this suggest a paradigm shift in AI research towards collaborative learning and knowledge transfer between models of varying capabilities?

The findings indeed hint at a potential paradigm shift towards collaborative learning and knowledge transfer in AI research:

From "bigger is better" to "smarter training": The traditional paradigm of relying solely on increasingly larger models could evolve to prioritize the development of more sophisticated training techniques that leverage the strengths of models of varying capabilities.
Ensemble learning and model distillation: We might see a surge in research on ensemble learning methods, where multiple weaker models are combined to achieve performance comparable to or even exceeding that of a single large model. Model distillation techniques, where knowledge from a larger model is compressed into a smaller one, will also likely gain more traction.
Decentralized and collaborative AI development: This approach could foster a more decentralized and collaborative AI research landscape. Researchers with limited resources can contribute by training specialized weaker models, which can then be leveraged to improve stronger models.
Lifelong learning and continual learning: The idea of weaker models "teaching" stronger models aligns well with the concepts of lifelong learning and continual learning, where models continuously learn and improve over time by incorporating new knowledge and experiences.
This potential paradigm shift is exciting as it could lead to:

More efficient use of resources: Reducing the dependence on massive datasets and computational power.
More robust and generalizable AI systems: Leveraging the diverse strengths and perspectives of multiple models.
A more inclusive and collaborative AI research community: Enabling wider participation from researchers with varying levels of resources.