[Compass for the AI Era] Paper Commentary Series
Reasoning to Learn from Latent Thoughts
Yangjun Ruan, Neil Band, Chris J. Maddison, Tatsunori Hashimoto
https://arxiv.org/abs/2503.18866
⭐️Story Explanation
The story of this video is about a fisherman grandfather teaching Nyanta why AI needs more data than humans, and clearly explaining the importance and innovation of a new AI technology called "reasoning to learn".
⭐️Point Explanation
1. Main Findings:
The most important finding is the demonstration of [improving data efficiency] using [latent thinking model]. In the MATH evaluation, the model trained with [latent thinking] achieved a 25.4% accuracy rate, significantly exceeding the 5.74% achieved with only raw data. In addition, through self-improvement learning using BoLT, the model was shown to gradually improve its ability from its own generated latent thoughts, proving that it can learn efficiently from less web text.
2. Methodology:
In the reasoning-to-learn approach, latent variable inference is used to model the latent thoughts Z behind observed data X. The model simultaneously learns p(Z,X) and q(Z|X) through next token prediction, and quality is improved using the EM algorithm with Monte Carlo sampling. Potential improvements include expanding the scope of unsupervised learning and introducing a hierarchical latent thought structure, which will lead to more complex inference efficiency.
3. Research limitations:
This research is limited to small-scale models of around 1B and mathematical texts with billions of tokens, and its application to large-scale language models and validation in general domains remain issues. It was also verified only in a data-constrained environment, and side effects of latent thought bootstrap (overfitting to specific domains) were observed. Addressing these limitations requires experimentation in diverse domains and the extension of the design of the latent thinking model.
4. Related Work:
This work extends previous work on the use of synthetic data generation to improve training efficiency and inference ability. In particular, it is innovative in that it provides a new perspective of "learning by thinking" in contrast to "learning to reason" and realizes self-improvement learning without reward signals. While previous work focuses on training for specific tasks, this work aims to improve the overall efficiency of language model pre-training.
5. Future Impact:
This work establishes a new paradigm in language model pre-training and paves the way for improving the trade-off between computational scaling and data efficiency. Future work is expected to apply the latent thinking model to general domains, extend it to multimodal data, and develop hierarchical latent thinking structures. In particular, it will have an impact on the design of new training infrastructures, where asynchronous synthetic data generation improves the efficiency of synchronous pre-training.
▶︎Qiita: https://qiita.com/compassinai
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