ROXAS

[ROXAS @roxasery]

どんな #ポケモン会えるかな ？

#ポケモン30周年 #PR @poke_times

[AI at Meta @AIatMeta]

Introducing DINOv3: a state-of-the-art computer vision model trained with self-supervised learning (SSL) that produces powerful, high-resolution image features. For the first time, a single frozen vision backbone outperforms specialized solutions on multiple long-standing dense prediction tasks.

Learn more about DINOv3 here: https://t.co/lQpKhJLTZQ

[ROXAS @roxasery]

@mishalsalim Please

[ROXAS @roxasery]

@basedonmoon Please

[ROXAS @roxasery]

@Jaydenfn86 SORA

[ROXAS @roxasery]

@ozgrozer Can I have a code? Thanks!

[Vincent Zhan @Vince2000_]

I have 100 Sora invite codes. Like, comment, and repost this post. Follow @Vince2000_ and @Martini373469 I will randomly select 100 from the comments.

[ROXAS @roxasery]

@Vince2000_ @Martini373469 Can I have a code?

[Ahmad @TheAhmadOsman]

Comparing & Contrasting Recent LLMs Architecture

> DeepSeek-V3/R1
> OLMo 2
> Gemma 3
> Mistral Small 3.1
> Llama 4
> Qwen3 (dense+MoE)
> SmolLM3
> Kimi 2
> GPT-OSS

Are 2025 LLMs really that different from each other?

MoE, MLA, GQA, sliding window, normalization games & more.

[Rohan Paul @rohanpaul_ai]

The paper shows a small model trained with reinforcement learning can outperform prompt only agents on machine learning engineering.

Most agents just prompt large models and search longer, but they do not learn from experience.

This work instead trains a 3B Qwen model with reinforcement learning, updating its decision rule from task feedback.

Challenge 1, actions take different time to run, so naive distributed training overcounts fast but weak ideas.

They fix this by weighting each update by action runtime, so slower high value runs matter.

Challenge 2, rewards are sparse, a near miss and a total failure look the same to the learner.

They add environment instrumentation, a separate frozen model inserts print lines into the code and awards small credit for milestones like loaded data or trained model.

These steady signals steer the agent away from metric gaming and toward better modeling, like simple feature engineering or stronger classifiers.

With these fixes plus a self improvement prompt, the small learner keeps improving over runs and often beats frontier models.

----

Paper – arxiv. org/abs/2509.01684

Paper Title: "Reinforcement Learning for Machine Learning Engineering Agents"

[ROXAS @roxasery]

research method比预想中的要难啊……加上之前在香港已经折腾地精疲力尽了，这五天过得简直就是灾难T_T事实上那些让你连参考资料都找不着的课才是真正难的课=_=

[ROXAS @roxasery]

太多时候，错过了就错过了

[ROXAS @roxasery]

@EssieZMY 自己评论自己？

[ROXAS @roxasery]

@EssieZMY 我还是觉得高调点的进步比较快……不过把别人教得比自己还明白什么的……及时暴露缺点才能及时去改正啊=_=

[ROXAS @roxasery]

@ElevenerT 其实只按上左下右交替着按就能打到宋朝……

[ROXAS @roxasery]

“想到这里，我就悲哀得难以自禁。因为，直子连爱都没爱过我的。”

[ROXAS @roxasery]

RT @roxasery: 而我现在所处的痛苦……几乎就是永恒了，我一直都是这么愚蠢

[ROXAS @roxasery]

我非常好奇当初的我是怎么想到把饭否和twitter连到一起的= =

[ROXAS @roxasery]

我要去见那高山。

[ROXAS @roxasery]

蠢死了我……给个陷阱就往里跳=_=