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From the 1 of 11 linked papers with an AI index.

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20242026
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11 papers

cs.CV2026

SD-MAR: Multi-image Analytical Reasoning via Synthetic Data and Reinforcement Learning

Shiyu Yuan, Sourav Sanjukta Bhabesh, Zhe Wang +3

The paper introduces SD-MAR, a synthetic-data framework and reinforcement‑learning fine‑tuning method to improve vision‑language models' ability to reason analytically across multi…

cs.LG2026

Reinforcing the Generation Order of Multimodal Masked Diffusion Models

Yidong Ouyang, Zhe Wang, Sourav Bhabesh +1

Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks. Recent research demonstrates that adaptive token generation order…

cs.AI2026

SQL-ASTRA: Alleviating Sparse Feedback in Agentic SQL via Column-Set Matching and Trajectory Aggregation

Long Li, Zhijian Zhou, Jiangxuan Long +5

Agentic Reinforcement Learning (RL) shows promise for complex tasks, but Text-to-SQL remains mostly restricted to single-turn paradigms. A primary bottleneck is the credit assignme…

cs.LG2026

DyJR: Preserving Diversity in Reinforcement Learning with Verifiable Rewards via Dynamic Jensen-Shannon Replay

Long Li, Zhijian Zhou, Tianyi Wang +7

While Reinforcement Learning (RL) enhances Large Language Model reasoning, on-policy algorithms like GRPO are sample-inefficient as they discard past rollouts. Existing experience…

cs.CV2026

ShotVerse: Advancing Cinematic Camera Control for Text-Driven Multi-Shot Video Creation

Songlin Yang, Zhe Wang, Xuyi Yang +7

Text-driven video generation has democratized film creation, but camera control in cinematic multi-shot scenarios remains a significant block. Implicit textual prompts lack precisi…

cs.LG2026

The Choice of Divergence: A Neglected Key to Mitigating Diversity Collapse in Reinforcement Learning with Verifiable Reward

Long Li, Zhijian Zhou, Jiaran Hao +9

A central paradox in fine-tuning Large Language Models (LLMs) with Reinforcement Learning with Verifiable Reward (RLVR) is the frequent degradation of multi-attempt performance (Pa…