Date
Publisher
arXiv
We present Aryabhata 1.0, a compact 7B parameter math reasoning model
optimized for the Indian academic exam, the Joint Entrance Examination (JEE).
Despite rapid progress in large language models (LLMs), current models often
remain unsuitable for educational use. Aryabhata 1.0 is built by merging strong
open-weight reasoning models, followed by supervised fine-tuning (SFT) with
curriculum learning on verified chain-of-thought (CoT) traces curated through
best-of-$n$ rejection sampling. To further boost performance, we apply
reinforcement learning with verifiable rewards (RLVR) using A2C objective with
group-relative advantage estimation along with novel exploration strategies
such as Adaptive Group Resizing and Temperature Scaling. Evaluated on both
in-distribution (JEE Main 2025) and out-of-distribution (MATH, GSM8K)
benchmarks, Aryabhata outperforms existing models in accuracy and efficiency,
while offering pedagogically useful step-by-step reasoning. We release
Aryabhata as a foundation model to advance exam-centric, open-source small
language models. This marks our first open release for community feedback
(https://huggingface.co/PhysicsWallahAI/Aryabhata-1.0); PW is actively training
future models to further improve learning outcomes for students.
What is the application?
Who age?
Why use AI?
Study design
