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Layer Importance for Mathematical Reasoning is Forged in Pre-Training and Invariant after Post-Training
Aadim Nepal, Safal Shrestha, Anubhav Shrestha +4
Large language models improve at math after instruction tuning, reinforcement learning, or knowledge distillation. We ask whether these gains come from major changes in the transfo…
Reinforcement Learning vs. Distillation: Understanding Accuracy and Capability in LLM Reasoning
Minwu Kim, Anubhav Shrestha, Safal Shrestha +2
Recent studies have shown that reinforcement learning with verifiable rewards (RLVR) enhances overall accuracy (pass@1) but often fails to improve capability (pass@k) of LLMs in re…
Warm Up Before You Train: Unlocking General Reasoning in Resource-Constrained Settings
Safal Shrestha, Minwu Kim, Aadim Nepal +2
Designing effective reasoning-capable LLMs typically requires training using Reinforcement Learning with Verifiable Rewards (RLVR) or distillation with carefully curated Long Chain…
Mathematical Reasoning in Large Language Models: Assessing Logical and Arithmetic Errors across Wide Numerical Ranges
Safal Shrestha, Minwu Kim, Keith Ross
Mathematical reasoning in Large Language Models (LLMs) is often evaluated using benchmarks with limited numerical ranges, failing to reflect real-world problem-solving across diver…