Ila R. Fiete

Qiyao Liang, Jinyeop Song, Yizhou Liu et al.

Weight-perturbation evolution strategies (ES) can fine-tune billion-parameter language models with surprisingly small populations (e.g., $N\!\approx\!30$), contradicting classical zeroth-order curse-of-dimensionality intuition. We also observe a second seemingly separate phenomenon: under fixed hyperparameters, the stochastic fine-tuning reward often rises, peaks, and then degrades in both ES and GRPO. We argue that both effects reflect a shared geometric property of fine-tuning landscapes: they are low-dimensional in curvature. A small set of high-curvature dimensions dominates improvement, producing (i) heterogeneous time scales that yield rise-then-decay under fixed stochasticity, as captured by a minimal quadratic stochastic-ascent model, and (ii) degenerate improving updates, where many random perturbations share similar components along these directions. Using ES as a geometric probe on fine-tuning reward landscapes of GSM8K, ARC-C, and WinoGrande across Qwen2.5-Instruct models (0.5B--7B), we show that reward-improving perturbations remain empirically accessible with small populations across scales. Together, these results reconcile ES scalability with non-monotonic training dynamics and suggest that high-dimensional fine-tuning may admit a broader class of viable optimization methods than worst-case theory implies.

Andrii Zahorodnii, Jasper J. F. van den Bosch, Ian Charest et al.

Traditional closed peer review systems, which have played a central role in scientific publishing, are often slow, costly, non-transparent, stochastic, and possibly subject to biases - factors that can impede scientific progress and undermine public trust. Here, we propose and examine the efficacy and accuracy of an alternative form of scientific peer review: through an open, bottom-up process. First, using data from two major scientific conferences (CCN2023 and ICLR2023), we highlight how high variability of review scores and low correlation across reviewers presents a challenge for collective review. We quantify reviewer agreement with community consensus scores and use this as a reviewer quality estimator, showing that surprisingly, reviewer quality scores are not correlated with authorship quality. Instead, we reveal an inverted U-shape relationship, where authors with intermediate paper scores are the best reviewers. We assess empirical Bayesian methods to estimate paper quality based on different assessments of individual reviewer reliability. We show how under a one-shot review-then-score scenario, both in our models and on real peer review data, a Bayesian measure significantly improves paper quality assessments relative to simple averaging. We then consider an ongoing model of publishing, reviewing, and scoring, with reviewers scoring not only papers but also other reviewers. We show that user-generated reviewer ratings can yield robust and high-quality paper scoring even when unreliable (but unbiased) reviewers dominate. Finally, we outline incentive structures to recognize high-quality reviewers and encourage broader reviewing coverage of submitted papers. These findings suggest that a self-selecting open peer review process is potentially scalable, reliable, and equitable with the possibility of enhancing the speed, fairness, and transparency of the peer review process.