图11 展示了增加 inference-time computation 带来的优势。 图11:Inference-time Scaling 结果 SANA 在 GenEval 上的精度随采样的增加不断提高。其次,推理时间缩放使较小的 SANA 模型能够匹配甚至超过较大模型的准确性 (1.6B + 缩放优于 4.8B)。这些结果揭示了 scaling up inference 的潜力。 唯一的限制是计算成...
强化学习先驱 Rich Sutton 在“The Bitter Lesson” 中说: One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that ...
Paper tables with annotated results for Bag of Tricks for Inference-time Computation of LLM Reasoning
affects their robustness to adversarial attacks. We studied a range of tasks using both static and adaptive attack methods, measuring the probability of attack success as a function of the amount of computation used by the model at inference. We see that in many cases, this probability decays—...
One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and lea...
ARTIFICIAL NEURAL NETWORK REDUCTION TO REDUCE INFERENCE COMPUTATION TIMETraining devices and methods for training an artificial neural network (ANN). The training device includes processing circuitry configured to transmit training data for the ANN and parameters for the ANN to an inference device. The ...
ETS: Efficient Tree Search for Inference-Time Scaling TLDR Test-time scaling has emerged as a new axis for improving model performance by leveraging additional computation at inference time in order to solve more challenging problems. One promising approach for scaling compute at test time is through...
Generic secure computation techniques are mainstream for solving secure neural network inference problems. In the remainder of this section, we discuss existing works into three categories: (a) MPC-based protocols, (b) Fully Homomorphic Encryption (FHE)-based protocols, and (c) Trusted Execution Env...
predicts a relevant subset of attention (heads) or MLP parameters in the next layer and only loads them for the computation hardware-efficient sparsity kernel fusion memory coalescing missing KV cache Limitations Too sensitive to large batch size Limited in the high-throughput setting, as KV ...
This paper is the first to propose valid inference tools, based on self-normalization, in time series expected shortfall regressions. In doing so, we propose a novel two-step estimator for expected shortfall regressions which is based on convex optimization in both steps (rendering computation easy...