Kaggle Orbit Wars 赛事揭示算力鸿沟：冠军模拟 150 亿步夺冠
Kaggle 聊了场强化学习比赛：赢家模拟 150 亿步，还蹭公司服务器集群，第二名只用了 1/8 算力，差在哪听他们自己讲。
Kaggle 播客 Beyond the Leaderboard 第一期讨论 Orbit Wars 竞赛中的算力差距。冠军 Isaiah Pressman 模拟了 150 亿步，比他过往竞赛高出两到三个数量级。Simon Jégou 的方案只用到冠军约 1/8 的算力，先用规则智能体进前 50、模仿学习进前 10，最后五天改用自博弈拿到第 2 名。冠军使用雇主的服务器集群训练，这呼应了 2020 年一项针对 171K 篇论文的研究发现的算力分配不均问题。
"A perfect illustration of the bitter lesson: the one with the most compute wins," notes @simon_jegou, who estimates his Orbit Wars solution used 1/8 the compute of the winner.
In the first episode of Beyond the Leaderboard, Kaggle's Addison Howard talks with three Orbit Wars competitors about the compute divide:
1️⃣ Isaiah Pressman simulated 15 billion steps on his way to victory. That's two to three orders of magnitude more than he could in past competitions. He says that with reinforcement learning, whatever your solution is, more compute probably makes it better.
2️⃣ Simon Jégou had the same realization, but perhaps too late given his comparatively limited compute. He reached the top 50 with rule-based agents and top 10 using imitation learning. With five days left, he started from scratch with self-play and took 2nd.
3️⃣ Gerardo Nahuel Del Toro spent his first month just fitting the game environment onto one GPU. He packed each piece of data into the smallest integer type that fit so the whole environment could run in parallel. Given the constraints, a top 2% finish was an accomplishment.
A 2020 study of 171K papers from top computer science conferences first identified the compute divide. As deep learning gained popularity, big tech and elite universities took a growing share of papers at top conferences, crowding out lower-ranked schools. The authors tied that gap to access to compute.
Orbit Wars shows it among individuals. Most Kagglers compete on their own time. Notably, the winner trained his solution on his employer's cluster.
Gerardo’s reaction (in the video) to losing to a cluster: he's not mad. Big hardware brings its own hard problems.
What's the best result you've gotten on limited hardware?
