Search Results for "math training"

...It emphasizes memory-efficient training strategies so you can train long-context or reasoning-dense models on commodity GPUs. The framework is also organized to help you compare training strategies (e.g., pure SFT vs. preference optimization) so you can see what actually moves metrics in math, code, and multi-step reasoning. For teams exploring open reasoning models, EasyR1 provides an opinionated yet flexible path from dataset to deployable checkpoints.

...The repository aims to make datasets easy to inspect and transform, with scripts for downloading, deduping, cleaning, and converting to formats like JSONL that slot into training pipelines. It highlights instruction-tuning and conversation-style corpora while also pointing to code, math, or domain-specific sets for targeted capabilities. Quality is a recurring theme: examples and utilities help filter low-value samples, enforce length limits, and split train/validation consistently so results are comparable. ...

...The evaluation pipeline measures speculative decoding performance across benchmark tasks such as math, coding, instruction-following, and chat-style datasets. Overall, it is useful for researchers and engineers studying faster language model inference through speculative decoding methods.

...Users can also hand-code parts of their models that demand better performance. Neural network inference is fast, but can be inaccurate on out-of-distribution data, and requires expensive training.