Back to AI Lab
HuggingFace Paper

Back to Bytes: Revisiting Tokenization Through UTF-8

Amit Moryossef, Clara Meister, Pavel Stepachev +1June 15, 2026

Summary

The authors propose UTF8Tokenizer, which maps bytes directly to token IDs and encodes control signals using old-school control bytes. This keeps embedding tables tiny, speeds up tokenization, and can be bolted onto existing models to improve convergence without changing how you run them.

Related Content

Are We Ready for RL in Text-to-3D Generation? A Progressive Investigation

This paper is a systematic exploration of reinforcement learning for text-to-3D generation, dissecting reward design, RL algorithms, data scaling, and hierarchical optimization. The authors introduce a new benchmark (MME-3DR), propose Hi-GRPO for global-to-local 3D refinement, and build AR3D-R1—the first RL-tuned text-to-3D model that improves both global shape quality and fine-grained texture alignment.

OPV: Outcome-based Process Verifier for Efficient Long Chain-of-Thought Verification

OPV (Outcome-based Process Verifier) is a verifier model that inspects the rationale steps of long chains-of-thought via summarized outcomes, combining the strengths of outcome-based and process-based verification. Trained with an active learning loop, rejection fine-tuning, and RLVR, OPV reaches strong F1 on OPV-Bench and outperforms much larger models like Qwen3-Max-Preview at detecting reasoning errors.

Long-horizon Reasoning Agent for Olympiad-Level Mathematical Problem Solving

This work presents a long-horizon reasoning agent for Olympiad-level math that uses an Outcome-based Process Verifier (OPV) to supervise and clean up very long chains-of-thought. By summarizing and checking reasoning segments rather than only final answers, and training OPV via iterative active learning and RLVR, the system achieves new SOTA on a held-out benchmark while reducing annotation cost.

T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground

T-pro 2.0 is an open-weight Russian large language model focused on hybrid reasoning: it can answer directly or emit explicit reasoning traces, and it’s optimized for low-latency inference via speculative decoding. Alongside the model, the authors release a Russian instruction corpus, a math benchmark, and an EAGLE-based inference stack, making it a practical foundation for Russian-language reasoning applications.