🟢 OpenAI 2w ago
⭐ Editor's Pick
OpenAI published GPT-Red, an internal-only automated red-teaming model trained via self-play RL to find and exploit prompt injection vulnerabilities at scale. GPT-Red achieved 84% attack success rate on an indirect prompt injection benchmark vs. 13% for human red-teamers on novel scenarios — and its outputs were fed back into GPT-5.6's training to harden it. The attacker earns reward for eliciting failures; defender models earn reward for resisting, creating a co-evolutionary loop. Training compute was at the scale of OpenAI's largest post-training runs. GPT-Red remains internal-only and is never deployed publicly.
SAP agreed to acquire Freiburg-based startup Prior Labs — developers of tabular foundation models (TFMs) — and committed over $1.18B over four years to transform it into a frontier AI lab specializing in structured business data prediction. The acquisition signals enterprise incumbents moving to build in-house AI capabilities distinct from LLMs: TFMs are trained on structured tables rather than language corpora, targeting prediction tasks across SAP's customer base.
A dense 25-minute reference post on neural scaling laws, tracing the lineage from Amari (1992) and Hestness (2017) through Kaplan et al. (2020) and Chinchilla (2022), explaining why the two papers disagree on compute-optimal allocation, and extending into data-constrained regimes with Muennighoff et al. (2023) and Lovelace et al. (2026). Includes toy simulation widgets and careful derivations of the parametric loss fitting. Particularly useful as a practitioner reference on how fitting choices—parameter rounding, loss normalization, fit region—cause large divergences in extrapolation.
Alec Radford (GPT, CLIP, Whisper), Nick Levine, and David Duvenaud released Talkie, a 13B model trained only on text published before 1931. The model's worldview is frozen at December 31, 1930: it doesn't know about World War II or the internet. Claude Sonnet was used to help test and judge the model's output. The r/ClaudeAI post (786 upvotes) drew discussion about what the model reveals about the common ancestor problem — every major LLM today shares overlapping training data, making true behavioral diversity impossible without deliberate data partitioning. Notable for the eval methodology: using a modern LLM to judge a historically-constrained one.
Google DeepMind proposes Decoupled DiLoCo, an extension of the DiLoCo distributed training framework designed for resilient training across heterogeneous or unreliable compute. No content snippet available beyond the title, but DiLoCo variants address the core challenge of large-scale training without tight synchronization.
MEM1 trains agents end-to-end via RL to compress and update an internal memory state at each step, maintaining constant context size across arbitrarily long multi-turn tasks. Unlike RAG or full-context retention, the memory management policy itself is learned. Demonstrated on multi-turn web and tool-use tasks; from MIT, accepted ICLR 2026.
Intuitor (ICLR 2026) trains LLMs to improve reasoning using only self-certainty as a reward signal—no labeled data, no external verifier, no human-crafted reward. The companion code release (RLIF framework) enables direct reproduction of the result that models can self-improve on reasoning benchmarks from internal feedback alone. Practically significant because it removes the dependency on curated verifiable datasets.