Results of the summarizing process for the arXiv paper: 2312.06585v1

In this paper, the authors propose a self-training method called ReST$^{EM}$ to fine-tune language models (LMs) on tasks where scalar feedback is available, such as math problems. The method involves generating samples from the model, filtering them using binary feedback, and fine-tuning the model on these samples through multiple iterations. The authors test ReST$^{EM}$ on advanced math reasoning and APPS coding benchmarks using PaLM-2 models and compare its performance with fine-tuning only on human data. They find that ReST$^{EM}$ scales well with model size and significantly outperforms fine-tuning only on human data. Additionally, they evaluate the performance of ReST$^{EM}$-fine-tuned models on the Big-Bench Hard suite of tasks and find no major degradation compared to the base model. Furthermore, they assess the problem-solving capabilities of their model by evaluating it on a Hungarian high school finals exam in mathematics and find that it performs well, surpassing all existing models except GPT-4. A key strength of ReST$^{EM}$ is its ability to generate multiple correct solutions for each problem, providing valuable additional training data compared to human-generated data which typically offers only a single solution per problem. Through their study, the authors demonstrate that ReST$^{EM}$ can substantially reduce dependence on human generated data and improve reasoning capabilities in language models.

• Self-training method called ReST$^{EM}$ proposed for fine-tuning language models (LMs) on tasks with scalar feedback
• Method involves generating samples, filtering with binary feedback, and multiple iterations of fine-tuning
• Tested on advanced math reasoning and APPS coding benchmarks using PaLM-2 models
• Outperforms fine-tuning only on human data, scales well with model size
• Performance evaluated on Big-Bench Hard suite of tasks, no major degradation compared to base model
• Problem-solving capabilities assessed on Hungarian high school finals exam in mathematics, performs well compared to existing models except GPT-4
• Ability to generate multiple correct solutions for each problem is a key strength, provides additional training data compared to human-generated data which typically offers only a single solution per problem
• Reduces dependence on human generated data and improves reasoning capabilities in language models

Introducing ReST$^{EM}$: A Self-Training Method for Fine-Tuning Language Models on Math Problems

In recent years, natural language processing (NLP) has seen a surge in the development of powerful language models such as GPT-4 and PaLM-2. These models have been used to great success in various tasks such as question answering, text summarization, and machine translation. However, they are still limited when it comes to advanced reasoning tasks that require understanding complex mathematical concepts. To address this issue, researchers from the University of Toronto recently proposed a self-training method called ReST$^{EM}$, which can be used to fine-tune language models on math problems where scalar feedback is available. In this blog article, we will discuss how ReST$^{EM}$ works and its performance on various benchmarks compared to fine-tuning only on human data.

How Does ReST$^{EM}$ Work?

ReST$^{EM}$, short for Reinforcement Self Training with Expectation Maximization (ReST$^{EM}$), is a self-training method for fine-tuning language models on tasks where scalar feedback is available. The main idea behind the method is to generate samples from the model using reinforcement learning (RL) techniques and filter them using binary feedback before fine tuning the model through multiple iterations.

The first step involves generating samples from the model by applying RL techniques such as Q Learning or Policy Gradient methods. The generated samples are then filtered using binary feedback based on whether they satisfy certain criteria or not - for example if they correctly solve a math problem or not. Once these samples have been filtered out, they are used to fine tune the model through multiple iterations until it reaches an optimal state where it can accurately solve math problems given scalar feedback.

Performance Evaluation

To evaluate the performance of ReST${^\text{EM}}$, the authors tested it on two different datasets: advanced math reasoning benchmark APPS coding benchmark and Big Bench Hard suite of tasks. They compared its performance with that of traditional fine tuning only on human data and found that ReST${^\text{EM}}$ significantly outperformed traditional methods in both cases - scaling well with increasing model size while maintaining accuracy levels comparable to those achieved by human data alone. Additionally, when evaluated against existing models like GPT 4 , their results showed no major degradation compared to base models while surpassing all other existing models except GPT 4 .

Furthermore, they also assessed its problem solving capabilities by testing it against a Hungarian high school finals exam in mathematics which revealed that their model performed well - surpassing all existing models except GPT 4 . This result was attributed mainly due to its ability to generate multiple correct solutions per problem thus providing valuable additional training data compared to human generated data which typically offers only one solution per problem .

Conclusion

In summary ,the authors demonstrate that their proposed self training method called ReSt${^\text{EM}} $can substantially reduce dependence on human generated data while improving reasoning capabilities in language models . Through their study ,they show that this technique can be effectively applied towards solving complex mathematical problems with scalar feedbacks available . Their results suggest that this approach could potentially open up new possibilities for further research into NLP applications involving advanced reasoning tasks