FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models
cs.LG, stat.CO, stat.ML
Submitted: 2026-09-28
Updated: 2026-09-28
Terminology
Sources
- GPT-4 Technical Report
- Preference-Guided Diffusion for Multi-Objective Offline Optimization
- Adaptive Diffusion Guidance via Stochastic Optimal Control
- Power-SMC: Low-Latency Sequence-Level Power Sampling for Training-Free LLM Reasoning
- Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models
- Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead
- LLaDA2.0: Scaling Up Diffusion Language Models to 100B
- Classifier-Free Guidance is a Predictor-Corrector
- Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
- Distributional Biases in Post-Training: A Markovian Analysis of Reasoning Trajectories
- Inference-Time Alignment for Diffusion Models via Variationally Stable Doob's Matching
- Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
- Split Gibbs Discrete Diffusion Posterior Sampling
- DASIP: Dynamic Test-Time Compute Scaling for Robot Control with Stochastic Interpolant Policies
- Inference-Time Scaling of Diffusion Language Models via Trajectory Refinement
- Noise Hypernetworks: Amortizing Test-Time Compute in Diffusion Models
- Learn to Guide Your Diffusion Model
- Discrete Diffusion Trajectory Alignment via Stepwise Decomposition
- Discrete Feynman-Kac Correctors
- What Exactly Does Guidance Do in Masked Discrete Diffusion Models
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks