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Kamran Chitsaz

2 accepted papers

2026

The Markovian Thinker

ICLR 2026poster

Reasoning LLMs suffer from quadratic compute growth as their context length increases, making reinforcement learning with verifiable rewards (RLVR) and test-time scaling prohibitively expensive. Prior work has tried to lighten the computational burden by shortening reasoning traces through pruning,…

Cited by 0SourceScholar
2024

Exploring Quantization for Efficient Pre-Training of Transformer Language Models

EMNLP 2024finding

The increasing scale of Transformer models has led to an increase in their pre-training computational requirements. While quantization has proven to be effective after pre-training and during fine-tuning, applying quantization in Transformers during pre-training has remained largely unexplored at sc…