aiqbench analysis
This arXiv preprint proposes a training change for fine-tuning language models on chain-of-thought math data. Soft-TF mixes teacher-forced embeddings with soft tokens computed in one parallel pass, adding no parameters or curriculum. The reported gains of +2.1 to +4.1 points on GSM8K are across several model families, but they come from a single unreviewed preprint, so readers should check whether they hold under independent replication and broader benchmarks.
Soft Teacher Forcing
arXiv:2610.17189v1 Announce Type: new Abstract: Soft tokens have so far been used to replace discrete chain-of-thought in latent reasoning methods. These approaches typically require multi-stage training and, in some cases, auxiliary modules, yet they still underperform supervised fine-tuning (SFT) on triples. In this paper, we ask whether soft tokens can be used to improve SFT itself, instead of as an avenue for latent reasoning. We introduce Soft Teacher Forcing (Soft-TF), a simple training algorithm that combines teacher-forced embeddings with soft tokens through random convex combinations. The soft tokens are computed in a single parallel forward pass over the ground-truth sequence. Soft-TF adds no parameters, no external model and no curriculum, and leaves inference unchanged. Experiments show that Soft-TF improves over SFT on GSM8K, MATH, MathQA and AQuA, by +2.1 to +4.1 points on GSM8K across seven Gemma, Llama and Mistral models.
Abstract:Soft tokens have so far been used to replace discrete chain-of-thought in latent reasoning methods. These approaches typically require multi-stage training and, in some cases, auxiliary modules, yet they still underperform supervised fine-tuning (SFT) on <input, chain-of-thought, output> triples. In this paper, we ask whether soft tokens can be used to improve SFT itself, instead of as an avenue for latent reasoning. We introduce Soft Teacher Forcing (Soft-TF), a simple training algorithm that combines teacher-forced embeddings with soft tokens through random convex combinations. The soft tokens are computed in a single parallel forward pass over the ground-truth sequence. Soft-TF adds no parameters, no external model and no curriculum, and leaves inference unchanged. Experiments show that Soft-TF improves over SFT on GSM8K, MATH, MathQA and AQuA, by +2.1 to +4.1 points on GSM8K across seven Gemma, Llama and Mistral models.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2610.17189 [cs.CL]
(or arXiv:2610.17189v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.17189 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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