Maksym Zhuravinskyi — accepted AI-conference papers

2025

Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional Video Generation

Agneet Chatterjee, Rahim Entezari, Maksym Zhuravinskyi, Maksim Lapin, Reshinth Adithyan, Amit Raj +3

NeurIPS 2025poster

Recent advances in video generation have enabled high-fidelity video synthesis from user provided prompts. However, existing models and benchmarks fail to capture the complexity and requirements of professional video generation. Towards that goal, we introduce Stable Cinemetrics, a structured evalua…

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2024

GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements

Alexander Havrilla, Sharath Chandra Raparthy, Christoforos Nalmpantis, Jane Dwivedi-Yu, Maksym Zhuravinskyi, Eric Hambro +1

ICML 2024poster

State-of-the-art language models can exhibit reasoning refinement capabilities on math, science or coding tasks. However, recent work demonstrates that even the best models struggle to identify *when and where to refine* without access to external feedback. In this paper, we propose Stepwise ORMs (*…

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2023

trlX: A Framework for Large Scale Reinforcement Learning from Human Feedback

Alexander Havrilla, Maksym Zhuravinskyi, Duy Van Phung, Aman Tiwari, Jonathan Tow, Stella Biderman +2

EMNLP 2023long main

Reinforcement learning from human feedback (\textbf{RLHF}) utilizes human feedback to better align large language models with human preferences via online optimization against a learned reward model. Current RLHF paradigms rely on Proximal Policy Optimization (\textbf{PPO}), which quickly becomes a…

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