LLM Post Training

Projects

QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs

ICLR 2026

QeRL is a framework that accelerates LLM training by combining NVFP4 quantization with LoRA. It reduces memory overhead, allows 32B model training on a single H100 GPU, and improves performance by leveraging quantization noise to

Fast-dLLM v2: Efficient Block-Diffusion Large Language Model

ICLR 2026

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Our approach introduces a novel training recipe that combines a block diffusion mechanism with a complementary attention mask, enabling blockwise bidirectional context modeling without sacrificing AR training objectives.

Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding

ICLR 2026

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We introduce a novel block-wise approximate KV Cache mechanism tailored for bidirectional diffusion models, enabling cache reuse with negligible performance drop.

Taming the Long-Tail: Efficient Reasoning RL Training with Adaptive Drafter

ASPLOS 2026

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TLT is a lossless acceleration framework for reasoning-oriented LLM RL training, introducing adaptive speculative decoding to eliminate long-tail generation bottlenecks. It achieves over 1.7× end-to-end speedup while fully preserving model quality and producing a high-quality draft model for efficient deployment.