GoBOED: Goal-Driven Bayesian Optimal Experimental Design for Decision-Focused Robustness
GoBOED is a new framework for Bayesian optimal experimental design (BOED) that replaces information-gain maximization with direct optimization for a specified downstream decision objective. It combines an amortized variational posterior surrogate with a differentiable convex decision layer to enable gradient-based, decision-focused design optimization. The authors prove that GoBOED gradients are insensitive to parameter directions irrelevant to the decision goal, formally justifying why goal-driven design achieves equivalent decision quality over a wider range of experimental designs. Empirical results across source localization, epidemic management, and pharmacokinetic control show improved alignment with decision objectives compared to goal-agnostic BOED.
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GGRO: Gradient-Guided Reward Optimization for inference-time LLM alignment
Researchers introduce Gradient-Guided Reward Optimization (GGRO), an inference-time alignment method that uses gradient signals from a reward model to inject 'nudging tokens' at high-uncertainty decoding steps, rather than relying on sampling-intensive re-ranking approaches like Best-of-N. The method monitors token-level entropy to detect distribution drift and steers generation trajectories directly, claiming improved robustness to reward hacking with minimal computational overhead. Experiments show gains across safety, helpfulness, and reasoning benchmarks compared to standard inference-time alignment baselines.
Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization
This paper addresses miscalibration in Gaussian process predictive distributions used for Bayesian optimization, focusing specifically on the lower tail relevant to minimization objectives. The authors introduce a framework for 'goal-oriented' spatial calibration below a threshold t, defining occurrence calibration and thresholded μ-calibration on sublevel sets. They propose tcGP, a post-hoc calibration method, and prove the resulting EI-based optimizer remains dense in the design space. Experiments on standard benchmarks show tcGP improves both lower-tail calibration and overall BO performance compared to standard and globally calibrated GP models.
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.
MG-ADSGD achieves optimal communication complexity for decentralized stochastic strongly convex optimization
Researchers propose Multi-Gossip Accelerated DSGD (MG-ADSGD), a decentralized stochastic optimization algorithm that simultaneously achieves accelerated dependence on both the condition number (√κ) and the network spectral gap (1/√(1-β)), a combination no prior stochastic method had attained. The algorithm couples gossip depth with mini-batch size so that additional communication rounds improve both consensus accuracy and gradient variance reduction. The resulting communication complexity is claimed to be the best currently known for decentralized stochastic strongly convex optimization up to logarithmic factors.
AXPO: Agent Explorative Policy Optimization Addresses Thinking-Acting Gap in Multimodal Agentic Reasoning
This paper identifies a structural asymmetry in agentic reasoning called the 'Thinking-Acting Gap,' where tool use is attempted in only ~30% of rollouts under standard RL training (GRPO), and all-wrong tool-using subgroups suppress learning signals. The authors propose AXPO (Agent eXplorative Policy Optimization), which fixes the thinking prefix and resamples tool calls for all-wrong subgroups, combined with uncertainty-based prefix selection. Evaluated across nine multimodal benchmarks on Qwen3-VL-Thinking at multiple scales, SFT+AXPO outperforms SFT+GRPO by +1.8pp on both Pass@1 and Pass@4 at 8B, with the 8B model surpassing the 32B baseline on Pass@4 using 4× fewer parameters.
Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches
This paper introduces an agentic framework where an LLM acts as an operations research expert, translating natural-language user prompts into structured updates ('patches') to deployed optimization models and selecting appropriate re-optimization techniques from a toolbox. The toolbox leverages primal information—historical solutions, valid inequalities, solver configurations, and metaheuristics—to accelerate re-optimization while preserving solution quality. Experiments on supply chain re-optimization and university exam scheduling demonstrate computational efficiency gains and improved interpretability through patch-based model modifications. The framework aims to reduce dependence on OR experts for maintaining dynamic decision-support systems.
GraphPO: Graph-based Policy Optimization reduces redundancy in LLM reasoning RL
GraphPO is a new reinforcement learning framework that represents reasoning rollouts as directed acyclic graphs rather than independent chains or trees, merging semantically equivalent reasoning paths into equivalence classes to share suffixes and reduce redundant exploration. The approach assigns efficiency advantages to incoming edges and correctness advantages to outgoing edges, deriving process supervision from outcome rewards. Experiments on three LLMs across reasoning and agentic search benchmarks show consistent improvements over chain- and tree-based baselines under equal token or response budgets. The method also provides theoretical guarantees on reduced advantage-estimation variance.
AGDO: Attention-guided denoising and optimization framework improves diffusion language model reasoning
Researchers propose AGDO, a framework that replaces random masking in diffusion large language models (dLLMs) with attention-guided denoising order and token weighting during fine-tuning and reinforcement learning. The work is motivated by an empirical finding that tokens with stronger attention to unmasked context are more stable and critical for reasoning. Experiments on math and coding benchmarks show AGDO outperforms existing post-training methods for dLLMs, advancing the case for attention-aware training in parallel-decoding language models.