People keep asking me: what's different about optimization in RL? Seemingly nothing — the pre-training stack just works (Adam, even SGD 👀 @saagnikkk). Bringing some answers from my last work (sorry for the delay — been cooking 🚀). We introduce ISO: Isospectral Optimization: an RLVR-native optimization stack. Built on one simple observation, spectral inheritance: RLVR can reuse the base model's spectrum and acquire new behavior purely through the singular frames. 🧩 Offline: ISO-Merger — consolidates RL experts into one model with no data, no rollouts, no OPD. Checkpoints only. ⚙️ Online: ISO-Optimizer — a drop-in wrapper on AdamW / Muon that matches AdamW's accuracy with ~2.7× fewer steps on Qwen3-8B-Base. 📄 🌐 🧵👇

Paper: Website with model responses and CoT: Authors: @BetleyJan @j_treutlein @jan_dubinski_ @HarryMayne5 @karolgalazka @nielsrolf1 @anna_sztyber myself.

🚀Excited to release PACE: A Proxy for Agentic Capability Evaluation! Evaluating LLM agents on benchmarks like SWE-Bench and GAIA is expensive, slow, and infrastructure-heavy, often costing $$$ and taking hours or days per model. ❓But do we always need to run full agentic evaluations? In PACE, we show that agentic benchmark performance can be accurately predicted from a small, carefully selected set of cheap non-agentic benchmark instances. PACE automatically selects proxy instances from existing benchmarks covering skills like instruction following, planning, tool use, reasoning, coding, retrieval, and multimodal understanding. Across 14 models, 4 agentic benchmarks, and 19 non-agentic benchmarks, PACE-BENCH achieves: ✅ 3.80% MAE for absolute score prediction ✅ 0.81 Spearman correlation for model ranking ✅ ~84% pairwise preference accuracy ✅ ~100× lower cost than target benchmark sampling Beyond prediction, PACE also reveals what capabilities different agentic benchmarks actually require, e.g., planning, verification, long-context aggregation, and instruction following. We hope PACE makes agentic evaluation cheaper, faster, and more accessible for model development, model selection, and routing :) 📃 Paper: 💻 Code: I'm incredibly grateful to have worked with @lintangsutawika, @Jiarui_Liu_, @lltjuatja, @JiayiiGeng, @lrzneedresearch, @daniel_js_lee, @Aditya_Soni_8, @Vincent92965015, @xiangyue96, and @gneubig .

Introducing EdgeBench, a benchmark designed to study how agents learn from environments over at least 12~72-hour runs. We find that performance follows a log-sigmoid function of environment interaction time with high precision. EdgeBench is built with three ingredients: - 🌍 Real & Diverse: 134 real-world tasks across 6 task categories, spanning scientific problems, professional knowledge work, software engineering, optimization, formal math, and games. - ⏳ Ultra-Long-Horizon: Each task supports 12–72 hours of agent work. Recorded human effort averages 57.2 hours. - 🔁 Informative Feedback: Agents receive real-world feedback for continuous improvement. After 38,000 hours of agent runs on EdgeBench, a scaling law for learning from environments emerges: - 📈 As agents interact with task environments over time, their aggregate performance is precisely fit by a log-sigmoid function. - 🧠 This phenomenon can be explained by an elegant theory of graph exploration. We are releasing an initial 51 of the 134 tasks, together with the full evaluation framework, to help advance long-horizon agent research. Check our blog & paper for more findings! Blog Paper GitHub Dataset Details below 👇🧵

We took a 30B model and split it in two to write tokens in parallel instead of one at a time. Introducing Nemotron-Labs-TwoTower: a diffusion language model from NVIDIA Research adapted from Nemotron-3-Nano-30B-A3B. Here’s how it works: one half holds the context, the other writes the tokens, with both reusing the pretrained model instead of training a new one from scratch. We found it kept 98.7% of the original model’s quality at 2.42× faster generation.