吴良超的学习笔记

This Spring Festival, DeepSeek pushed large language model discussion to new heights. Even in an 18-tier small town’s New Year atmosphere, unexpected technological ripples were hidden. My cousin shouts dialect at her phone “Write me a New Year greeting”, my nephew chats with Douba’s gentle, understanding “school beauty”, even the alley’s Spring couplet vendor learned to customize gold-patterned designs with generative AI - these digital ripples in daily life, like a silent enlightenment movement, wove “large language models” into this small town’s capillaries.

Two years ago AI was like a bronze giant statue, trembling with computational roar when ingesting data, only able to process stellar data in first-tier city data centers. Today’s large models have become flowing streams, infiltrating along 5G base stations into county auto repair shops’ QR scan systems, kneaded into Kuaishou streamers’ dialect scripts, even hibernating in elderly phones’ voice assistants coughing once to remind medication. From “brute force aesthetics” hundred-billion parameter arms race, to MoE architecture deftly slicing computational cake, from millions-of-dollars lab aristocracy, to DeepSeek-R1 tearing open commoner admission with $6M training cost - this evolution isn’t just technological leap, but metaphor for tech narrative shifting from “monologue from the altar” to “dialogue with humanity”: when large models learn cost-optimized ballet on GPU remains, technology’s capillaries finally touched daily life’s heartbeat.

今年的春节，deepseek 把大模型的讨论热度推向了高潮，即使是在十八线小城的年味里，也藏着些意料之外的科技褶皱。表妹用方言对着手机喊 “给俺写段拜年词”，小侄子在忙着跟豆包里的声音温柔、善解人意的 “校花” 聊天，连巷口的春联摊主都学会了用生成式设计定制烫金纹样 —— 这些烟火气里的数字涟漪，像一场无声的启蒙运动，将 “大模型” 三个字编织进了这座十八线小城的毛细血管

两年前的 AI 还像一座青铜巨像，吞吐数据时浑身震颤着算力的轰鸣，只能在北上广深的数据中心里吞吐星辰；而今的大模型已化作游走的溪流，沿着 5G 基站浸润到县城修车铺的扫码系统、揉进快手主播的方言脚本，甚至蛰伏在老人机的语音助手里咳嗽一声提醒吃药。从 “暴力美学” 的千亿参数军备竞赛，到 MoE 架构轻巧切开算力蛋糕的刀锋，从耗资数千万美元的实验室贵族，到 DeepSeek-R1 用 600 万美元训练成本撕开的平民入场券 —— 这场进化不仅是技术的跃迁，更是科技叙事从 “神坛独白” 转向 “人间对话” 的隐喻：当大模型学会在显卡残骸上跳成本最优化的芭蕾，技术的毛细血管终于触到了烟火人间的心跳

The previous mix-ranking article mainly introduced the basic approach from “Ads Allocation in Feed via Constrained Optimization” and extended the discussion to open problems in mix-ranking. That paper solves request-level insertion rules but doesn’t consider practical constraints like adload, i.e., the proportion of ads shown is limited.

When considering adload constraints, we can’t just compare values within requests; we need to maximize value across requests or at the session level. For example, showing more ads on high-value requests and fewer or no ads on low-value requests to maximize revenue under adload constraints. This paper “Hierarchically Constrained Adaptive Ad Exposure in Feeds” provides a solution approach. The paper’s overall approach still uses beam search and the generator+evaluator mix-ranking paradigm, but incorporates overall constraints into the online solving process. Compared to conventional approaches that control adload independently from mix-ranking, this is a better approach worth reading~

之前写的混排文章，主要介绍了《Ads Allocation in Feed via Constrained Optimization》里的基本做法，同时拓展讨论了混排中的一些开放性问题，这篇 paper 解决的问题是 request 维度的插入规则，但是没有考虑到一些比较实际的约束如 adload，即出广告的比例是有限的

而如果考虑到 adload 的约束，就不能只考虑 request 内的价值比较了，而是要考虑到 request 之间或者说 session 维度的价值最大化了，如在某些高价值请求上多出，低价值请求上少出或不出，以此达到 adload 约束下收入最大化。这篇 paper《Hierarchically Constrained Adaptive Ad Exposure in Feeds》为这个问题提供了一个解决思路；paper 整体做法还是 beam search 和 generator + evaluator 的混排范式，但是在这个过程中会把整体约束考虑到这个在线的求解过程中；相较于常规做法把控 adload 独立在混排之外，是一个比较好的思路，值得一读～

Recently I’ve been researching multi-channel bidding problems. Quoting Google Ads’ Power More Conversions and Value through Cross-Channel Bid Optimization:

Traditionally, advertisers have applied automated bidding to campaigns that target a single channel. For example, they might use a bid strategy that maximizes conversion value on separate campaigns for Search, Display, and Video. But there are limitations to this siloed approach. But multi-channel bid optimization can help you to drive better results compared to single-channel bid optimization by maximizing marginal CPA or ROAS in each and every auction

Simply put, when a campaign runs across more traffic positions simultaneously, budget marginal utility can be better optimized. This is intuitive - with richer traffic inventory, the same budget can theoretically achieve better efficiency. This is similar to “universal delivery” products recently launched by various domestic media platforms. These products provide lower-barrier solutions for advertisers, saving budget allocation or bid setting across channels, while platforms use algorithmic capabilities to improve budget efficiency.

From a technical perspective, multi-channel raises two questions:

1. Is unified bidding optimal? If not, how to do per-channel bidding
2. Should budget be explicitly allocated to each channel

The multi-channel examples above are all within one large platform, where budget and bidding across channels can be easily shared. Another common multi-channel definition is cross-platform, e.g., advertisers running on both Google and Meta, where budget and bidding clearly can’t be shared. From the advertiser’s perspective, how to optimally allocate is also worth discussing.

This article mainly discusses the former: budget allocation and bidding when running on multiple channels within the same platform. Also briefly mentions research on cross-platform scenarios.

最近一段时间在研究 multi-channel bidding 的问题，套用 Google Ads 的 Power More Conversions and Value through Cross-Channel Bid Optimization 是这么定义这个问题的

Traditionally, advertisers have applied automated bidding to campaigns that target a single channel. For example, they might use a bid strategy that maximizes conversion value on separate campaigns for Search, Display, and Video. But there are limitations to this siloed approach. But multi-channel bid optimization can help you to drive better results compared to single-channel bid optimization by maximizing marginal CPA or ROAS in each and every auction

简单来说，就是一个计划在更多流量位上同时投放，能够更好优化预算的边际收益（marginal CPA or ROAS），这个也很好理解，因为有了更丰富的流量库存，同一笔预算效率理论上是能做到更优的；这跟国内当前各个媒体平台近年提的 “通投”、“全站投放” 等产品，本质上也是类似的。这类产品广告主提供了使用门槛更低的产品，省去了广告主在不同 channel 分配预算或设置出价的过程，同时平台能够通过算法能力把预算的效率变得更高

从技术视角上来看，这里的 multi-channel 有两个问题值得讨论

（1）统一出价是否是最优的？如果不是，分 channel 出价要怎么做
（2）预算是否要显式分配到各个 channel

上面讨论的 multi-channel 的例子都是在一个大的平台下的 channel，这种情况下各 channel 的预算和出价是比较容易共享的；而另外一个常见的 multi-channel 的定义是跨平台的，比如说很多广告主会同时在 google 和 meta 两个 channel 投放，这个时候显然预算和出价是不能共享的；那站在广告主的视角下，怎么分配是最优的也是一个值得讨论的问题

本文主要讨论前者，即在同一平台的多个 channel 同时投放时，budget allocation 和 bidding 的相关问题；同时会提一下在不同平台（channel）投放时的一些研究，祝开卷有益~

最近听了情感播客《面基》的两期关于中年的播客：《中年，人生的第二座山》和 《中年之路上的四种觉醒》后，也看了提及的几本相关的书:《第二座山》、《中年之路》、《中年觉醒》以及一些研究报告；颇有感触，想写点东西来记录一下，于是有了这篇文章

每次读到这类内容，都会有一种错觉，似乎听完后就可以合理化自己躺平，给自己不努力找一个借口；因为一命二运三风水、命运无常，在随机性面前的我们似乎无能为力（比较扎心的是，这的确是一个不争的事实）；但这并不意味着我们要躺平，原因在之前写的 《做一个清醒的傻瓜》中也提到了：“在努力还没达到一定程度前，我们连面对随机性的机会都没有，或者说幸存者偏差也是有门槛的，当你的能力不足时，进入决赛圈的资格都没有”

本文是写给自己的心理按摩，是为了让自己在 “尽人事” 后，能够更坦然地 “听天命”；是为了能在面对人生的第二座山的时候，能够更加从容；祝开卷有益～

在此前的文章 《关键跃升：心态》中，介绍了书的上半部分，即心态上的需要做出的一些改变，本文主要讲的是剑法法部分，即管理者需要扮演好四种角色 “鼓手、教练、政委、指挥”，这部分也涉及到比较多的方法论，值得一读～

最近在看《关键跃升》，里面的不少观点还是颇有启发的，用作者的话来说，这是 “一本适合中国管理者的管理读本”；但无论是管理者还是被管理者，笔者觉得都有必要去了解一下管理的相关内容，才能更好地扮演自己在职场中的角色

书名的跃升，粗糙来说就是通过 “自己完成任务” 跃升到 “通过别人完成任务”；书里把这部分拆成了两部分：心法和剑法，翻译一下就是心态和行动上需要做出的改变；心法包括了 “责任、沟通、关系，自我” 四大块，剑法则是强调管理者需要扮演好四种角色 “鼓手、教练、政委、指挥”；整本书的内容脉络较为清晰，比较通俗易懂，值得一读；本文主要是心法部分～

过去较长一段时间，一直有这种感觉：对于某个事情或想法，“听到了” 跟 “理解了” 中间有一段距离，“理解了” 跟 “实现了” 中间又有一段距离，“实现了” 跟 “做成了” 中间则会有一段更大的距离。简单来说，就是 “知” 与 “行” 之间存在着巨大的鸿沟，且仅站在 “知” 这一端的人往往是看不到这个差距所在的

最近听到的这期播客 《E35. 知识的缝隙》，更为系统和深入地探讨了这个现象及其底层原因，也让笔者对上面的 “距离” 有了更深的理解，用播客的话来说，这些距离就是那些认知的缝隙。播客从费曼学习法开始说起，逐步揭开了那些看似光滑的认知弧线下存在的缝隙，而躬身入局、把手弄脏，去填补这些认知的缝隙，也许就会有想不到的收获

写这篇的文章的过程，亦是笔者在填补自己认知的缝隙的过程，文章可能有点发散，祝开卷有益～