Drifting Preference Optimization (DrPO) for One-Step Text-to-Image Generators
DrPO is a new online preference fine-tuning method designed specifically for deterministic one-step text-to-image generators like SD-Turbo and SDXL-Turbo, which are difficult to align with standard RLHF methods that require policy likelihoods or differentiable reward gradients. The method samples candidates per prompt, ranks them with a target reward, and synthesizes a feature-space update direction via a non-parametric dipole preference field plus a reference drift from the frozen base model. Because the reward is used only for ranking, DrPO supports black-box and non-differentiable reward functions while keeping inference as a single forward pass. Evaluations on HPSv3 and GenEval show improved alignment over reward-gradient-free baselines and a 3.51× reduction in training compute by eliminating reward-model backpropagation.
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DRPO: Smooth divergence regularization replaces hard masking in LLM RL training
A new arXiv preprint proposes Divergence Regularized Policy Optimization (DRPO), a method that replaces the hard trust-region mask used in DPPO with a smooth advantage-weighted quadratic regularizer on policy shift. The approach addresses a known weakness in PPO and GRPO where importance ratios poorly proxy distributional shift in long-tailed vocabularies, and in DPPO where gradient signals are discarded rather than corrected at trust-region boundaries. Experiments across model scales, architectures, and precision settings show improved stability and efficiency in LLM RL post-training.
Preference Tuning LLMs with Direct Preference Optimization Methods
A Hugging Face blog post surveys Direct Preference Optimization (DPO) and related preference tuning methods for aligning large language models. The post covers the landscape of DPO variants and their practical application via the TRL library. It serves as a technical reference for practitioners implementing RLHF alternatives.
Finetune Stable Diffusion Models with DDPO via TRL
Hugging Face's TRL library adds support for DDPO (Denoising Diffusion Policy Optimization), enabling reinforcement learning-based finetuning of Stable Diffusion models. This extends TRL's RLHF tooling beyond language models to image generation, allowing reward-driven optimization of diffusion models. The post demonstrates practical usage of the new DDPO trainer within the TRL ecosystem.
DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization
DRIFT is a training framework that bridges online RL and offline SFT for multi-turn LLM optimization by exploiting the theoretical equivalence between KL-regularized RL and importance-weighted supervised learning. It decouples rollout generation from policy optimization: trajectories are sampled from a fixed reference policy offline, weighted by return-based importance scores, and used for weighted SFT. Empirically, DRIFT matches or exceeds multi-turn RL baselines while retaining the efficiency and simplicity of standard supervised fine-tuning. Code is publicly released.
ZPPO: Teacher-in-prompt training method outperforms distillation and GRPO for small vision-language models
Researchers introduce Zone of Proximal Policy Optimization (ZPPO), a training method inspired by Vygotsky's zone of proximal development that embeds teacher guidance in prompts rather than policy gradients or logit imitation. On hard questions where student rollouts fail, ZPPO constructs Binary Candidate-included Questions (BCQ) and Negative Candidate-included Questions (NCQ) to help the student discriminate correct from incorrect responses, with a replay buffer that recirculates hard questions until mastered. Evaluated on the Qwen3 family (0.8B–9B) with a 27B teacher across a 31-benchmark suite covering VLM, LLM, and video tasks, ZPPO outperforms both distillation and GRPO baselines, with the largest gains at the smallest model scale. The method addresses a known failure mode of RL training where zero-reward rollouts produce no gradient signal.
Preference Optimization for Vision Language Models
This Hugging Face blog post covers the application of Direct Preference Optimization (DPO) to vision-language models (VLMs). It likely discusses how preference learning techniques originally developed for text-only LLMs can be adapted to multimodal settings. The post addresses training methodology for aligning VLMs with human preferences across both visual and textual modalities.
General Preference Reinforcement Learning (GPRL): Bridging Online RL and Preference Optimization for Open-Ended Tasks
GPRL proposes a new alignment framework that replaces scalar reward models with a General Preference Model (GPM) embedding responses into k skew-symmetric subspaces to capture multi-dimensional, intransitivity-aware preferences. The method computes per-dimension group-relative advantages, normalizes across axes, and uses a closed-loop drift monitor to detect and correct single-axis reward hacking during training. Starting from Llama-3-8B-Instruct, GPRL achieves a 56.51% length-controlled win rate on AlpacaEval 2.0 and outperforms SimPO and SPPO on Arena-Hard, MT-Bench, and WildBench. The work directly addresses the gap between verifiable-reward online RL (strong on math/code) and preference optimization (strong on open-ended tasks).
Direct Preference Optimization Beyond Chatbots
A Hugging Face blog post explores applications of Direct Preference Optimization (DPO) outside of conversational AI contexts. The post appears to survey or analyze how DPO, a technique for aligning language models with human preferences, can be applied to non-chatbot domains. The body content is unavailable, limiting assessment of specific claims or findings.