3arXiv cs.LG (Machine Learning)·
ARROW: First online stochastic variance reduction algorithm for MMD and CORAL domain adaptation losses
A new arXiv preprint introduces ARROW (Adaptive vaRiance Reduction via Online reWeighting), the first online stochastic variance reduction algorithm for the maximum mean discrepancy (MMD) and CORAL domain adaptation loss functions. Prior SVR algorithms for these losses were offline-only and incompatible with streaming, distributed, or incremental learning settings. ARROW maintains moving average references of alignment statistics and adaptively reweights incoming minibatches, with a relaxed reweighting scheme to keep the optimization tractable. Experiments show competitive performance with offline algorithms on runtime, variance reduction, and target domain accuracy.
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PSDA: Variance-reduced domain adaptation via paired sampling for distribution-matching objectives
A new arXiv preprint introduces Paired Sampling for Domain Adaptation (PSDA), a stochastic variance reduction technique designed for distribution-matching losses like correlation alignment and maximum mean discrepancy that lack finite-sum structure. PSDA constructs quadruplets of observations within and across domains by solving linear assignment problems to minimize expected gradient variance. Experiments on three domain shift datasets show improved target domain accuracy over related methods.
ASRD: Training-free anchor-guided revocable decoding for diffusion LLMs improves accuracy and throughput
A new arXiv preprint introduces ASRD (Anchor Supervised Revocable Decoding), a training-free framework for improving decoding quality in diffusion large language models. The method addresses error propagation and local error reinforcement in revocable decoding by separating trusted 'anchor tokens' (identified via temporal consistency) from uncertain candidates, then applying anchor-guided generation and anchor-perturbed verification. Experiments on math and coding benchmarks show up to 6.4% accuracy improvement and 7.2× inference throughput gains over remasking baselines.
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.
ACPO: Adaptive Clip Policy Optimization improves RLVR training for LLM reasoning
A new arXiv preprint provides theoretical analysis of Reinforcement Learning from Verifiable Rewards (RLVR) updates, identifying off-policy degree and gradient expectation as key factors governing update dynamics. The authors show that differences in gradient steps per rollout substantially affect importance sampling ratio distributions and which tokens dominate updates. Based on this analysis, they propose Adaptive Clip Policy Optimization (ACPO), which adjusts clipping boundaries per token group by empirical variance of importance sampling ratios, outperforming DAPO and CISPO baselines on 3B and 7B models across math, tabular QA, and logic benchmarks.
ADAS: Attention-Discounted Adaptive Sampler improves parallel decoding for masked diffusion language models
Researchers propose ADAS, a training-free reranking rule for masked diffusion language model decoding that addresses token interaction failures in parallel token commitment. The method greedily penalizes candidates that attend strongly to already-selected uncertain positions, using attention weights as soft marginal penalties rather than hard constraints. Evaluated on LLaDA-8B-Base and Dream-7B-Base across GSM8K, MATH500, HumanEval, and MBPP, ADAS improves low-NFE performance by 9–10 percentage points on average when plugged into existing samplers with only 3.1% runtime overhead.
CARV: Compute-Aware Variance Reduction for Diffusion Teacher Gradient Estimation
CARV is a hierarchical Monte Carlo estimation framework that reduces gradient variance when using frozen pretrained diffusion models as teachers in downstream pipelines such as text-to-3D distillation and data attribution. The approach amortizes expensive upstream computation (rendering, simulation, encoding) over cheap diffusion-noise resamples, augmented by timestep importance sampling and stratified-inverse-CDF construction. In text-to-3D experiments, CARV delivers 2–3× effective compute multipliers; in single-step distillation, it cuts gradient variance by an order of magnitude but does not improve FID, revealing that MC variance is not the bottleneck in that regime.
AdaSR: Adaptive streaming reasoning framework with Hierarchical Relative Policy Optimization
Researchers introduce AdaSR, a framework enabling large reasoning models to reason incrementally during streaming input (e.g., audio/video) rather than waiting for complete context, then perform final deliberation once the stream ends. The core contribution is Hierarchical Relative Policy Optimization (HRPO), which decomposes policy optimization into streaming and deep reasoning phases with fine-grained per-phase advantage assignment, integrating format, accuracy, and latency-aware rewards. Experiments show AdaSR improves the tradeoff among reasoning accuracy, computational efficiency, and streaming latency over supervised fine-tuning baselines. Code is publicly released.
MaLoRA and MaRA: Selective state-space adapters improve multi-hop reasoning over LoRA
A new arXiv preprint proposes two adapter families — MaLoRA (token-level dynamic scaling via Mamba recurrence) and MaRA (context-level segment retrieval via cross-segment state tracking) — as improvements over standard LoRA for language model reasoning. Evaluated on three frozen backbones (Qwen-2.5-7B, Llama-3.1-8B, Gemma-2-9B) and two multi-hop QA benchmarks (MuSiQue, 2WikiMultihopQA), the methods yield average gains of +6.8 F1 (+10.5% relative) over LoRA, with up to +18.2% relative improvement on the hardest configuration. Token-level gains also transfer to RULER QA-2 under length-stress conditions.