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The economic angst many Americans feel has much to do with how the economic pie is being divided. From WWII to Y2K, workers received close to two-thirds of the pie, while capital received the other one-third. Since then, China’s rapid entry into the global economy, the Global Financial Crisis, the pandemic, and other forces have put labor on its heels, pushing the split closer to fifty-fifty. Measurement issues matter, and the exact shares are reasonably debated, but the direction is not. And it's not hard to imagine artificial intelligence supercharging this trend. Many Americans fear it will.

📱 You want to train an agent that operates a phone. But real devices are costly, hard to reset, and hard to auto-verify, while mock environments alone are too simplistic to transfer to real phones. That dilemma has long blocked phone-agent training. The PhoneBuddy team refused the either-or and combined both. They first give Qwen3.5-4B a shared foundation with supervised fine-tuning over 950k action steps from real and mock environments. Then they blend real-app reinforcement learning fifty-fifty with PhoneWorld, a mock environment that reconstructs runnable Android apps from real GUI traces. The mock side auto-checks success with rule-based verifiers, while the real side has Gemini 3.1 generate rubrics and a large model score the trajectories, a clean division of labor. The payoff: average success on 150 real-phone tasks rose from 36.67% to 45.33%, and AndroidWorld climbed from 60.3% to 83.2% across stages, with this tiny 4B open model beating Gemini 3.1 Pro on that benchmark. The authors show mock and real are complementary rather than substitutable, while honestly noting that cross-app workflows remain the biggest open challenge. Title: PhoneBuddy: Training Open Models for Agentic Phone Use URL: #PhoneAgents# #ReinforcementLearning#