Papers by Azal Ahmad Khan
Accelerating LLM Reasoning via Early Rejection with Partial Reward Modeling (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are increasingly relied upon for solving complex reasoning tasks. |
| Approach: | They propose to use Process Reward Models to scale inference time compute by generating in parallel . they propose to provide early signals that enable the rejection of suboptimal candidates before full generation of step is complete. |
| Outcome: | The proposed method achieves 1.4 – 9 reduction in inference FLOPs without degrading final performance. |
Sem-DPO: Mitigating Semantic Inconsistency in Preference Optimization for Prompt Engineering (2026.findings-acl)
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Anas Mohamed, Azal Ahmad Khan, Xinran Wang, Ahmad Faraz Khan, Shuwen Ge, Saman Bahzad Khan, Ayaan Ahmad, Ali Anwar
| Challenge: | Direct Preference Optimization (DPO) is an off-policy alternative to RL for automatic prompt engineering, but its token-level regularization leaves semantic inconsistency unchecked as prompts that win higher preference scores can still drift away from the user’s intended meaning. |
| Approach: | They propose a variant of Direct Preference Optimization that preserves semantic consistency while maintaining its simplicity and efficiency. |
| Outcome: | The proposed model outperforms state-of-the-art prompt optimization baselines and several DPO variants on three standard text-to-image prompt-optimization benchmarks and three language models. |