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PaperclipPerfector  5mo ago (text post)   28225 thread views

Culture War Roundup for the week of June 30, 2025

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It can be true that Microsoft is replacing workers with AI, slimming down a bloated management apparatus and that they're also hiring more cheap H1Bs to watch over the AIs or for internal factional reasons.

I may be in the minority here but I still believe that AI is a big, big thing and advancing fast. No plateau, just steady growth and new avenues to advance down. Two recent papers.

https://arxiv.org/abs/2506.24119

Using SPIRAL, self-play on zero-sum games produces reasoning capabilities that transfer broadly. Training Qwen3-4B-Base on Kuhn Poker alone achieves 8.6% improvement on math and 8.4% on general reasoning, outperforming SFT on 25,000 expert game trajectories. Analysis reveals that this transfer occurs through three cognitive patterns: systematic decomposition, expected value calculation, and case-by-case analysis. Multi-game training (TicTacToe, Kuhn Poker, Simple Negotiation) further enhances performance as each game develops distinct reasoning strengths. Applying SPIRAL to a strong reasoning model (DeepSeek-R1-Distill-Qwen-7B) can still lead to 2.0% average improvement. These results demonstrate that zero-sum games naturally develop transferable reasoning capabilities, highlighting a promising direction for autonomous reasoning development.

https://arxiv.org/abs/2505.03335

we propose a new RLVR paradigm called Absolute Zero, in which a single model learns to propose tasks that maximize its own learning progress and improves reasoning by solving them, without relying on any external data. Under this paradigm, we introduce the Absolute Zero Reasoner (AZR), a system that self-evolves its training curriculum and reasoning ability by using a code executor to both validate proposed code reasoning tasks and verify answers, serving as an unified source of verifiable reward to guide open-ended yet grounded learning. Despite being trained entirely without external data, AZR achieves overall SOTA performance on coding and mathematical reasoning tasks, outperforming existing zero-setting models that rely on tens of thousands of in-domain human-curated examples. Furthermore, we demonstrate that AZR can be effectively applied across different model scales and is compatible with various model classes.

Two major papers on fully autonomous learning, in coding and reasoning via playing simple games respectively. Admittedly both are on a small scale, using small models and little compute. The former used maybe $1000 worth of compute when the big boys have billions to spend. However, recursive AI self-improvement isn't science fiction, it's eminently possible, presumably it's being done right now on a large scale.

Are people really going to be better at programming than a machine that has thousands, millions of years of experience? Surely not, just like they're not going to be better at chess or go or starcraft.

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