7 papers
Training Reasoning Models on Saturated Problems via Failure-Prefix Conditioning
Minwu Kim, Safal Shrestha, Anubhav Shrestha +1
As Reinforcement Learning with Verifiable Rewards (RLVR) substantially improves the reasoning abilities of large language models (LLMs), a new bottleneck emerges: more training pro…
On the Limits of Layer Pruning for Generative Reasoning in Large Language Models
Safal Shrestha, Anubhav Shrestha, Aadim Nepal +2
Recent work has shown that layer pruning can effectively compress large language models (LLMs) while retaining strong performance on classification benchmarks, often with little or…
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…
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…
Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds
Minwu Kim, Sidahmed Benabderrahmane, Talal Rahwan
This research presents a predictive model to identify potential targets of activist investment funds--entities that acquire significant corporate stakes to influence strategic and…