LFPO: Likelihood-Free Policy Optimization for Masked Diffusion Models

Chenxing Wei, Jiazhen Kang, Hong Wang, Jianqing Zhang, Hao Jiang, Xiaolong Xu, Ningyuan Sun, Ying He, F. Richard Yu, Yao Shu, Bo Jiang

Paper | GitHub

• Algorithm (LFPO): Introduces LFPO, which overcomes the likelihood intractability in Diffusion LLMs by directly optimizing denoising logits via contrastive positive/negative trajectories, achieving SOTA performance with significantly faster inference
• Theory: Proves the theoretical equivalence of continuous Flow Matching and discrete Masked Diffusion, justifying efficient trajectory rectification beyond policy gradients

Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation

Chenxing Wei, Hong Wang, Ying He, Zhongxiang Dai, Bo Jiang, F. Richard Yu, Yao Shu

Paper | GitHub

• Algorithm (ROSA2): Introduces ROSA2, which reformulates test-time adaptation as a co-adaptation framework that jointly optimizes interaction context and model parameters to significantly accelerate convergence speed
• Theory: Rigorously proves that semantic refinement acts as a pre-conditioner to strictly reduce parameter shift and guarantee faster convergence to the optimal policy

Test-Time Policy Adaptation for Enhanced Multi-Turn Interactions with LLMs

Chenxing Wei, Hong Wang, Ying He, Yao Shu, Fei Yu

Paper | GitHub

• Paradigm (T2PAM): Proposes a paradigm shifting alignment from offline training to test-time inference, utilizing conversational feedback for real-time policy updates
• Algorithm (ROSA): Introduces ROSA, a lightweight algorithm that performs single-step, analytical parameter updates for efficient in-conversation self-correction
• Theory: Proves monotonic error reduction at each turn and guarantees cumulative convergence to the user’s optimal preference

ReDit: Reward Dithering for Improved LLM Policy Optimization

Chenxing Wei, Jiarui Yu, Ying Tiffany He, Hande Dong, Yao Shu, Fei Yu

Paper | OpenReview | GitHub

• Algorithm (ReDit): a method that injects zero-mean random noise into rewards to smoothen the landscape, enabling continuous and stable gradient estimation.
• Theory: proves that reward dithering effectively mitigates gradient anomalies (vanishing/exploding) and significantly accelerates convergence.

PAFT: Prompt-Agnostic Fine-Tuning

Chenxing Wei, Yao Shu, Mingwen Ou, Ying Tiffany He, Fei Richard Yu

Paper | GitHub

• Algorithm (PAFT): Introduces PAFT, which minimizes the divergence between predictions from full prompts and “pattern-free” inputs, effectively decoupling task reasoning from specific instruction syntax.
• Theory: Theoretically guarantees reduced generalization error under prompt distribution shifts and empirically achieves state-of-the-art robustness.

Flexora: Flexible Low-Rank Adaptation for Large Language Models

Chenxing Wei†, Yao Shu†, Ying Tiffany He, Fei Yu

Paper | GitHub

• Algorithm (Flexora): Introduces Flexora, a framework that treats layer selection as a Hyperparameter Optimization (HPO) problem. It employs unrolled differentiation to automatically learn a policy that identifies and adapts only the most critical layers for specific downstream tasks.
• Theory: Provides theoretical insights into how automated, flexible layer selection effectively mitigates overfitting and enhances generalization compared to uniform adaptation.