Jiajun Fan

CS Ph.D. student at UIUC. I work on RL post-training for generative models — making diffusion/flow models and multimodal reasoning LLMs continuously self-improve with minimal human supervision. Previously: 24 Atari world records, 500× more data-efficient than Agent57, ICLR 2023 Oral (rank 5/4176).

📰 Latest News

• Apr 2026 AcceptSeveral papers accepted at ICML 2026 (Seoul, Jul 6–11). See you in Seoul 🇰🇷
• Apr 2026 Finish🇧🇷 Presented CESAR & SP-VLA at ICLR 2026, Rio de Janeiro, Apr 23–27.
• Jan 2026 Accept2 papers at ICLR 2026 — CESAR & SP-VLA. See you in Rio 🇧🇷
• Sep 2025 Accept2 papers at NeurIPS 2025 — ADRPO & VarCon. See you in San Diego 🌊
• Jun 2025 AcceptPaper accepted at IEEE TPAMI: PRANCE.
• Feb 2025 AcceptPaper accepted at ICLR 2025: ORW-CFM-W2 (Flow Matching self-evolution).
• Jan 2025 ServiceReviewer: ICLR 2024–26, NeurIPS 2022–25, ICML 2023–26, CVPR 2026, AAAI 2025, AISTATS 2025, KDD 2024.
• Aug 2024 🎓 Started Ph.D. at UIUC CS (GPA 4.0/4.0).
• Jan 2023 Oral · Top 5%LBC at ICLR 2023, ranked 5/4176 — broke 24 Atari world records.

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🔥 Featured Research

ICLR 2026

CESAR: Process Rewards for Audio LLM Reasoning

First to show test-time inverse scaling in Audio LLMs — and fix it with process rewards via online RL.

🏆 SOTA MMAU · Beats Gemini 2.5 Pro

NeurIPS 2025

ADRPO: Adaptive Regularization for Generative Model RLHF

Sample-level adaptive divergence regularization — 2B model outperforms 12B competitors.

🚀 2B SD3 > FLUX 12B & SANA 4.8B

ICLR 2025

ORW-CFM-W2: Online RLHF for Flow Matching

First collapse-free online RL framework for flow matching models with W2 regularization.

✨ First online RLHF for flow matching

🏅

ATARI WORLD
RECORDS

×24

ICLR 2023 · Oral

LBC: Superhuman Atari — 24 World Records

Breaking 24 Atari human world records with 500× less data than prior SOTA (Agent57).

🏅 Rank 5/4176 · 10,077% mean human performance

📄 Selected Publications

* = first/co-first author  ·  Full list on Google Scholar  /  Publications page

ICLR 20262026

Incentivizing Consistent, Effective and Scalable Reasoning Capability in Audio LLMs via Reasoning Process Rewards Project Page ▾ Abstract

CESAR resolves test-time inverse scaling in Audio LLMs by rewarding the reasoning process via GRPO, achieving SOTA on MMAU — outperforming Gemini 2.5 Pro and GPT-4o Audio.

J. Fan*, R. Ren, J. Li, R. Pandey, P.G. Shivakumar, I. Bulyko, A. Gandhe, G. Liu, Y. Gu

CESAR: process-reward RL (GRPO) resolving test-time inverse scaling in Audio LLMs — models produce hallucinatory reasoning without proper guidance; CESAR fixes that.

🏆 SOTA on MMAU Test-mini · Outperforms Gemini 2.5 Pro & GPT-4o Audio

📄 Paper 🔗 Project 📎 arXiv

ICLR 20262026

SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration ▾ Abstract

SP-VLA introduces action-aware model scheduling and spatio-semantic token pruning for VLA model acceleration, achieving 1.5× lossless speedup on LIBERO and 2.4× speedup on SimplerEnv.

Y. Li, Y. Meng, Z. Sun, K. Ji, C. Tang, J. Fan, X. Ma, S.-T. Xia, Z. Wang, W. Zhu

Action-aware model scheduling + spatio-semantic token pruning for VLA acceleration.

⚡ 1.5× lossless speedup (LIBERO) · 2.4× speedup (SimplerEnv)

NeurIPS 20252025

Adaptive Divergence Regularized Policy Optimization for Fine-tuning Generative Models Project Page ▾ Abstract

ADRPO introduces sample-level adaptive divergence regularization for RLHF — high-value samples get more freedom, poor samples get stronger constraints. Plug-and-play on any RL method.

J. Fan*, T. Wei, C. Cheng, Y. Chen, G. Liu

ADRPO: sample-level adaptive divergence regularization — high-value samples get more freedom, poor samples get stronger constraint. Plug-and-play on top of any RLHF method.

🚀 2B SD3 surpasses 4.8B & 12B models · Generalizes to LLMs & audio reasoning

📄 Paper 🔗 Project 📎 arXiv

NeurIPS 20252025

Variational Supervised Contrastive Learning ▾ Abstract

VarCon reformulates supervised contrastive learning as variational inference, achieving SOTA 79.36% Top-1 accuracy on ImageNet-1K with ResNet-50.

📊 SOTA 79.36% Top-1 on ImageNet-1K with ResNet-50

Z. Wang, J. Fan, T. Nguyen, H. Ji, G. Liu

VarCon: supervised contrastive learning as variational inference — posterior-weighted ELBO replaces pairwise comparisons.

ICLR 20252025

Online Reward-Weighted Fine-Tuning of Flow Matching with Wasserstein Regularization Project Page ▾ Abstract

ORW-CFM-W2 is the first online RLHF method for flow matching — no human data, no likelihood estimation. Wasserstein regularization maintains generation diversity.

🥇 First online RLHF for flow matching · Collapse-free W2 regularization

J. Fan*, S. Shen, C. Cheng, Y. Chen, C. Liang, G. Liu

ORW-CFM-W2: first online RLHF for flow matching — no human data, no likelihood, no collapse. W2 regularization keeps generation diverse.

Preprint2025

Fine-tuning Flow Matching Generative Models with Intermediate Feedback Project Page ▾ Abstract

AC-Flow introduces actor-critic with intermediate feedback for flow matching — reward shaping + dual-stability mechanism + Wasserstein regularization enables robust SD3 fine-tuning without collapse.

⚙️ Actor-critic with step-level reward · Stable SD3 fine-tuning without collapse

J. Fan*, C. Cheng, S. Shen, X. Zhou, G. Liu  ·  Under Review

AC-Flow: actor-critic with intermediate feedback for flow matching — reward shaping + dual-stability + Wasserstein regularization. Robust fine-tuning on SD3 without collapse.

TPAMI 20262026

PRANCE: Joint Token-Optimization and Structural Channel-Pruning for Adaptive ViT Inference ▾ Abstract

PRANCE jointly optimizes token pruning and structural channel pruning for adaptive ViT inference, achieving significant speedup while maintaining accuracy.

⚡ Joint token + channel pruning · Adaptive ViT inference · IEEE TPAMI 2026

Y. Li, C. Tang, Y. Meng, J. Fan, Z. Chai, X. Ma, Z. Wang, W. Zhu  ·  IEEE TPAMI

ICLR 2023
Oral2023

Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection Project Page ▾ Abstract

LBC introduces a learnable hybrid behavior mapping and bandit meta-controller for exploration control in deep RL, breaking 24 Atari human world records with 500× less data than prior SOTA.

J. Fan*, Y. Zhuang, Y. Liu, J. Hao, B. Wang, J. Zhu, H. Wang, S.-T. Xia

LBC: learnable hybrid behavior mapping + bandit meta-controller. Unified framework for exploration control in deep RL.

🏅 Ranked 5/4176 · 10,077% mean human score · 24 world records · 500× data efficiency

ICML 20222022

Generalized Data Distribution Iteration Project Page ▾ Abstract

GDI shows that optimizing the training data distribution is the key lever for superhuman RL efficiency. Provides a unified framework that subsumes diverse RL algorithms as special cases.

J. Fan*, C. Xiao

GDI: optimizing the data distribution is the key to superhuman RL efficiency. Unified framework for diverse RL algorithms.

📈 Agent57 beaten with 500× less data & 2× avg performance

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🕸️ Research Paper Network

Hover a node to highlight connections. Papers are grouped by research theme.

🔬 Research Interests

⚡ Impact at a Glance

💡 Research Vision

Making AI Systems That Improve Themselves

Today's AI is frozen after training. I work to change that: AI that never stops getting better, with progressively less human scaffolding.

🏅 Awards & Academic Service

📅 Conference Deadlines

Key AI/ML venue deadlines I track — for the full list see ccfddl.com.

📬 Contact

Happy to discuss research, internships, or collaborations. Best reached by email.
📧 jiajunf3@illinois.edu  ·  🏛 Siebel Center for CS, UIUC  ·  CV  ·  💼 LinkedIn  ·  🔬 ORCID

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