joint-training

ChronoSSM introduces an autoregressive State Space Model that jointly models events and timestamps, showing that joint training improves temporal recoverability without degrading content generation quality.

This paper proposes Joint Speech-Text Interleaved Pretraining (JSTIP), a pretraining strategy that constructs word-level and segment-level interleaved speech-text sequences to improve ASR entity accuracy and reduce the modality gap between speech and text, showing competitive performance on domain adaptation and zero-shot speech question answering.

Chronicle is a 324M-parameter decoder-only transformer pretrained from scratch on both natural language and time series, achieving competitive performance on NLU and time series classification tasks, and setting new state-of-the-art for frozen-embedding time series classification on UCR/UEA datasets.

This paper introduces ICRL, a framework that jointly trains a solver and critic with reinforcement learning to internalize critique guidance, enabling the solver to improve without external critique. It uses distribution calibration and role-wise group advantage estimation, achieving 6-7 point gains over GRPO on agentic and mathematical reasoning tasks.