![[2505.20674] PonderLM: Pretraining Language Models to Ponder in Continuous Space](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2505.20674] PonderLM: Pretraining Language Models to Ponder in Continuous Space
Summary
PonderLM introduces a novel approach to language model training by incorporating a 'pondering' phase, enhancing cognitive processing during token generation, leading to improved performance on various benchmarks.
Why It Matters
This research highlights a significant advancement in language model training techniques, potentially improving the efficiency and effectiveness of AI systems in understanding and generating human-like text. By enabling models to 'ponder', it opens new avenues for self-supervised learning without the need for extensive human annotations.
Key Takeaways
- PonderLM enhances language models by integrating a pondering phase during token generation.
- The method allows models to learn through self-supervised learning without human annotations.
- PonderPythia models outperform standard Pythia models on multiple benchmarks.
- The approach demonstrates that smaller models can rival larger ones with effective training techniques.
- The research contributes to the ongoing development of more efficient AI systems.
Computer Science > Computation and Language arXiv:2505.20674 (cs) [Submitted on 27 May 2025 (v1), last revised 20 Feb 2026 (this version, v3)] Title:PonderLM: Pretraining Language Models to Ponder in Continuous Space Authors:Boyi Zeng, Shixiang Song, Siyuan Huang, Yixuan Wang, He Li, Ziwei He, Xinbing Wang, Zhiyu Li, Zhouhan Lin View a PDF of the paper titled PonderLM: Pretraining Language Models to Ponder in Continuous Space, by Boyi Zeng and 8 other authors View PDF HTML (experimental) Abstract:Humans ponder before articulating complex sentence elements, enabling deeper cognitive processing through focused effort. In this work, we introduce this pondering process into language models by repeatedly invoking the forward process within a single token generation step. During pondering, instead of generating an actual token sampled from the prediction distribution, the model ponders by yielding a weighted sum of all token embeddings according to the predicted token distribution. The generated embedding is then fed back as input for another forward pass. We show that the model can learn to ponder in this way through self-supervised learning, without any human annotations. Experiments across three widely used open-source architectures-GPT-2, Pythia, and LLaMA-and extensive downstream task evaluations demonstrate the effectiveness and generality of our method. On 9 downstream benchmarks, our pondering-enhanced Pythia models significantly outperform the official Pythia models. No...
