Search Results: LLMTraining

As LLM training data harvesting floods the web, site administrators are fighting back by blocking generic User-Agent headers, forcing a reckoning with crawler transparency and resource management.

A new paper introduces EGGROLL, a low‑rank perturbation technique that turns the traditionally expensive Evolution Strategies into a viable tool for training billion‑parameter neural networks. By replacing full‑rank noise with a pair of small matrices, the method cuts memory and compute costs while matching the performance of classic ES on reinforcement learning and large‑language‑model benchmarks.

New research from UT Austin, Texas A&M, and Purdue suggests that large language models can exhibit a digital form of 'brain rot' when overexposed to junk data from social platforms. For developers and AI leaders, it’s a warning shot: data quality isn’t just a tuning detail—it’s the difference between a reliable co-pilot and a manipulative, brittle system.

A prominent technical blogger has implemented aggressive blocking against HTTP requests with generic User-Agent strings, citing an unsustainable flood of crawlers harvesting data for LLM training. This move highlights the escalating tension between website operators and the opaque, resource-intensive scraping fueling AI models.

Anthropic agrees to pay $1.5 billion to settle a class-action lawsuit alleging it trained its Claude AI on pirated books, marking the largest copyright recovery in history. The ruling establishes critical boundaries for AI development while forcing the industry to confront ethical data sourcing.

Facing the challenge of improving language model writing without verifiable metrics, Moonshot AI adopted an unconventional approach inspired by baseball statistician Bill James. By developing targeted, imperfect rubrics to guide reinforcement learning, Kimi K2 achieved top-tier qualitative performance while avoiding reward hacking pitfalls.