# Claude rewrites the optimizer mid-training: MIT paper code, 218k people saw the launch post, 16 days and only 345 stars · GitHub open-source project

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> The MIT CSAIL brand plus a well-phrased new angle, "the optimizer rewrites itself during training": the author's long X post, the advisor's repost, the X trending topic, and Sakana's Robert Lange's breakdown brought the first wave; the official MIT CSAIL account push on 11-02 brought the second.

[GitHub open-source project](https://stringers.io/en/github/index.md) · written 11-6

xingjian-bai/opal · xingjian-bai

# Claude rewrites the optimizer mid-training: MIT paper code, 218k people saw the launch post, 16 days and only 345 stars

**What it is** A PyTorch utility. It wraps your optimizer, and during training Claude Code periodically looks at the training data and edits optimizer code such as the learning rate and update rules.

**Why it blew up** An MIT CSAIL badge plus a catchy new claim, "the optimizer rewrites itself during training." An X long-post from the author, a repost by the advisor, an X trending topic, and a breakdown by Sakana's Robert Lange drove the first wave. 11-02 The official MIT CSAIL account post drove the second.

**C** **How you could apply it** Don't copy the optimizer itself. Move the pattern of "an agent embedded in a component that was hardcoded, editing it while it runs" to lower-barrier places (database tuning, crawlers, CI, fine-tuning callbacks). Copy its "paste it to your agent and it installs" approach and its free dry-run mode.

C grade: the stars came mostly from the MIT brand, the advisor, and official school posts, which followers can't borrow. Even with those, 218k views only turned into 345 stars. The trial bar is high and the ceiling is low. What's worth learning is the idea and how it's packaged. Circulating in small circles

Data (updated daily) · Repo created 2026-10-21 · 358 stars · 7 days +104

GitHub · stars

Data 2026-10-23 → 2026-11-08 (17 days, 17 observations). Points before 10-28 come from deeply / archive backfill, not every day has one

OPAL is a PyTorch package that came with a research paper by MIT CSAIL PhD student Xingjian Bai and advisor Vincent Sitzmann, among others. With one line of code you wrap your optimizer. During training, every so often Claude Code looks at the training data and then edits the optimizer's source code. The repo was created in Oct 21, and by Nov 6 it had 345 stars and 21 forks (GitHub daily numbers). The launch post got a lot of views but few stars, and the gap between those two numbers shows where stars like this actually come from. Below: what it is, where the stars came from, and what you can borrow if you want to build an open-source project that gets stars.

### Oct 21: a long post asking "Can an optimizer think for itself?"

On Oct 21 2026 UTC 15:59, Bai Jianjian (on X as @SimulatedAnneal) posted a 9-part long thread with a 24-second animation. The first part reads:

> Current optimizers are static during training. What if the optimizer could think and react while the network trains? We let an agent watch the training dynamics and rewrite the optimizer's source code as training goes: a reactive optimizer.

The closing line is bold: "In the near future, optimization decisions may belong to agents, not humans."

By 11-06 the post had 218k views, 1.5 thousand likes and 1.2 thousand bookmarks. Sitzmann (MIT associate professor) retweeted it the same day, and X generated a trending topic from it: "MIT's OPAL Lets AI Rewrite Optimizers During Neural Network Training." The next day, Sakana AI's Robert Lange posted a long commentary comparing it to Google's learned optimizer VeLO. The author also posted on Hacker News that day: 32 points, 3 comments, never reached the front page.

About 145 stars in two days (10-23 on GitHub).

### What it is

Training a neural network means first fixing a "recipe": which optimizer, what learning rate, how it decays, how gradients are clipped. The recipe is locked before training starts and stays the same until the end.

OPAL lets the recipe change mid-training. You write your training script as usual and wrap only the optimizer:

- Every so often, it hands statistics such as loss and gradients to Claude Code;

- Claude Code decides whether to rewrite two small programs: the optimizer itself (how each step turns gradients into parameter updates) and an analysis probe it wants to look at;

- There's a lot it can change: per-group update rules, learning rate and schedule, momentum, clipping, guards against loss spikes, and even swapping in Muon partway through. It is not allowed to touch the model, data, loss function, or step count.

The README results table: FID for SiT-B/2 image generation drops from 35.7 under the official recipe to 23.6, and LightningDiT-B/1 drops from 3.22 to 2.31. It also reports one miss: on modded-nanogpt a run reached 3.2878, worse than the existing record. Each number comes from a single run, and the author concedes the agent's decisions may differ from run to run.

By the blog's numbers: the LightningDiT run made about 100 decisions, which at Claude Opus 5.5 list price cost $6.42, while the training run itself took roughly 74 H100 hours (about $510).

### Where the stars came from: two pushes, two steps

| Date | What happened | stars (GitHub daily count) |
|---|---|---|
| 10-21 | Repo created, blog live, author's long X thread, advisor retweet, X trending topic, HN 32 points | — |
| 10-22 | Robert Lange's long commentary; 310 PyPI downloads that day | — |
| 10-23 | AI news sites across the web start re-reporting it | 145 |
| 10-24 | arXiv paper posted | 181 |
| 10-25 to 11-02 | No new pushes | 187 → 255, +1 to +21 per day |
| 11-02 | MIT CSAIL official LinkedIn (184k followers) and Facebook posts; 1,033 LinkedIn reactions | 255 |
| 11-03 to 11-04 | Push picks up, arXiv posts a second version | 269 → 327,11-04, +58 in a single day |
| 11-05 to 11-06 | Decline | 339 → 345 |

Both steps line up with one push each: first the author plus advisor, then the school's official account. For nine days in between nobody pushed, and stars rose only a few a day.

Another number tells the same story: the PyPI package `opal-optimizer` went from 10-22 to 11-05, about 976 downloads in total (pypistats), and its download curve spikes in sync with the star spikes. The repo has no open issues. Several hundred people saw the news and pip-installed it to try, but perhaps only a few dozen actually ran it on training jobs (my inference).

Apart from X, LinkedIn, and dozens of AI news sites (Chinese and Russian ones included), I found no other sources: no paid ads, no Product Hunt launch, no mainstream media coverage.

### Why this one

**The brand.** MIT CSAIL and Sitzmann on the byline, with the school's official account posting it. The author's own GitHub has only 37 followers. The other starred repo is code from a CVPR paper, with 55 stars. The stars didn't come from the author's own GitHub following. They came from academic X and the MIT name.

**A new idea that's easy to explain.** "The optimizer rewrites its own source code during training," plus "let Claude be the optimizer": that's an easy thing to repost on X. Earlier "let AI tune training automatically" projects, such as karpathy's autoresearch, Sakana's AI Scientist, and hyperparameter search, all run many trials outside training, compare results, and revise. OPAL runs once and edits mid-training. Robert Lange's endorsement was about exactly this difference.

**Riding a trend.** karpathy/autoresearch came out in March this year and now has nearly 100k stars, making "agents doing ML research automatically" a hot topic. OPAL is a new angle on that line.

**Easy to try after reading.** Indie devs can copy this part directly:

- The first screen of the README is a prompt you can paste straight into Claude Code, Codex, or Cursor. It has your agent install the package, wire it into your training script, and run a comparison first;

- `backend="keep"` is a dry-run mode that doesn't call the agent and costs nothing. Its output matches the unwrapped run bit for bit, so people feel safe trying it;

- `opal estimate` reports before training how long it will take and how much it will cost. `max_cost_usd` sets a cost ceiling;

- `opal report` produces a one-page HTML that lists every change made and why;

- GPU machines without internet access can still work: the agent runs on a laptop and passes messages through a shared folder.

The project went from first release to v0.3.10 in two weeks. Few paper codebases reach this level of completeness.

### Who it's competing with

| Peers | Scale (GitHub stars, 11-06) | Compared with OPAL |
|---|---|---|
| karpathy/autoresearch | 99,305 | An agent repeatedly edits training code from outside and runs many trials to compare results; OPAL edits only the optimizer, within a single training run |
| Sakana AI Scientist / v2 | 14,949 / 7,444 | Fully automated research, with many outer-loop evaluations |
| OpenEvolve (an open-source reproduction of AlphaEvolve) | 7,717 | Evolutionary search over algorithm code, across many runs |
| Schedule-Free / Prodigy | 2,334 / 474 | Hand-written adaptive learning rates |
| WecoAI/aideml | 1,611 | ML engineering agent that writes the whole codebase |
| Google VeLO / learned_optimization | 816 | Trains a learned optimizer on huge meta-training runs; OPAL trains nothing and has the LLM improvise on the spot |
| Optuna / Ray Tune / W&B Sweeps | wide adoption | hyperparameter search, which means running many trials |

Among repos created in the last 60 days, there's no second project doing "an agent rewriting the optimizer during training," and I found no copies either.

### If it were you

**C. Its "embed the agent inside a fixed component" idea and the "read it and you can try it" packaging are worth borrowing, but copying it as-is won't easily get you stars.** OPAL's stars came mainly from MIT's name, a professor's reshare, and the school's official posts, none of which you can borrow. Even with those, 218k views only turned into 345 stars and under a thousand downloads. The bar is high: you need a GPU training job, a Claude account, and the willingness to pay for an agent to meddle with training, so few people can actually try it.

**Who it's for** : people doing ML research or training engineering with a live training job already, or developers who know some "hand-written system component" well and want to build an open-source project in the agent space.

**Who it's not for** : indie devs who want a project with thousands of stars in a few weeks. Building your own "agent rewrites the optimizer" without experiment results or a paper behind it will be hard to believe.

**How to do it** : don't compete with it on optimizers. Move its pattern to places with a lower bar and more people who can actually try it. OPAL's pattern: find a module that currently runs on hard-coded rules, expose an interface of one or two code blocks, let the agent watch and edit while it runs, and wrap it with a dry-run mode, a cost cap, and a change report. Places this could go:

- Databases: let the agent watch slow queries live and change indexes and config;

- Crawlers and scraping jobs: let the agent adjust retries, rate limits, and parsing rules based on failure rates;

- Frontend builds and CI: let the agent tune caching and parallelism based on build times;

- In ML, closest to OPAL: the callback version of Hugging Face Trainer, data sampling mixes, and learning rates for fine-tuning jobs. Far more users do these than "train from scratch."

For each, copy its "paste this prompt to your agent" install flow and its dry-run mode.

**How to get users** : follow its sequence, but with things you can actually get. A before-and-after that's easy to follow (animation or screenshot), plus one long X thread that opens with a question nobody has asked ("why are configs frozen before startup?"). Write the HN title the way it did, one sentence saying what it does. Get a writer in your field who covers this kind of thing to share it once. OPAL itself shows that views aren't stars. The try-it bar is what matters, and something that runs on a laptop in five minutes converts far better (inferred).

**When to go before the window closes** : "an agent embedded in a component that edits it live" is still a new framing, and I haven't seen anything similar in the last two months. It stops being new once Claude Code or Codex ships this kind of "watch it in the background" feature, or a major lab publishes something similar. Within six months is probably safe (inferred).

**What would show I got it wrong** : (1) If after November people open issues on OPAL and use it in other projects, and downloads settle into a steady few dozen a day instead of spiking with the news, then it has real users, and a machine learning project built on it deserves a B. (2) If you port it to another component and two weeks after launch it's still single-digit stars, the idea doesn't sell without the paper's backing. (3) If OPAL or someone else quickly ships a "general version" that supports any component, this opening closes.

---

Data and sources: GitHub repo and daily star counts (10-23 to 11-06); README and blog xingjianbai.com/reactive-optimizers (read 11-06); arXiv 2610.33826; the author's launch thread on X (views and likes as seen on 11-06); X trending topics; Robert Lange's explainer thread; MIT CSAIL LinkedIn post; PyPI downloads from pypistats.org (10-22 to 11-05); Hacker News post; competitor stars from GitHub (11-06). Places marked "inferred" are estimates.

[GitHub repo ↗](https://github.com/xingjian-bai/opal)

**Research notes: competitor table, acquisition evidence, full research report**

Competitors

| Name | Where | Launched | Scale | How it differs from this one |
|---|---|---|---|---|
| karpathy/autoresearch | Open source | 2026-03-06 | 99,305 stars | An agent repeatedly edits the training code in an outer loop and runs many experiments; OPAL only edits the optimizer and changes it live within a single training run |
| Sakana AI Scientist / ShinkaEvolve | Open source | 2024-08 / 2025-09 | 14,949 / 1,499 stars | Fully automated research / program evolution; many evaluations in an outer loop, not a wrapper inserted into the training loop |
| OpenEvolve / Google AlphaEvolve | Open source | 2025-05 | 7,717 stars | Evolutionary search over algorithm code, across many runs |
| Google VeLO / learned_optimization | Open source | 2021-2022 | 816 stars | Meta-training at scale bakes the optimizer into the weights; OPAL is zero-shot, with its meta-knowledge coming from LLM pretraining |
| Schedule-Free / Prodigy and other adaptive optimizers | Open source | 2023-2024 | 2,334 / 474 stars | Hand-written adaptive rules; OPAL has an agent reason its way to rewriting any update rule |
| Optuna / Ray Tune / W&B Sweeps / PBT | Other | 2017- | wide adoption | Hyperparameter search needs many trials; OPAL trades LLM tokens for GPU runs, in a single training run |
| WecoAI/aideml | Open source | 2024-04 | 1,611 stars | ML engineering agent that writes the full codebase in an outer loop |

Where users come from

- **Verified** **author's X thread** : @SimulatedAnneal 10-21 nine-tweet thread + animation, 217.8K views, 1.5K likes, 1.2K bookmarks

- **Verified** **Professor's endorsement + X Grok Trending** : Vincent Sitzmann reshared the same day; X trending topic "MIT's OPAL Lets AI Rewrite Optimizers" (10-21)

- **Verified** **Field influencer explainer** : Robert Lange (Sakana) 10-22 long post, comparing it to VeLO

- **Verified** **MIT CSAIL official social** : 11-02 CSAIL LinkedIn (184k followers) 1033 reactions / 34 comments + Facebook video, matching 11-03~04 star gains of +14/+58 and 132 PyPI downloads that day

- **Verified** **AI news aggregators** : aiheadlinehub, glonce, AISignal, TheNextGenTechInsider, airebao, arxivdaily and others, 10-23~11-04 reposts

- **Verified** **HN** : post with 32 points and 3 comments, 10-21; did not reach the front page (hackernews library)

- **Verified** **arXiv / alphaXiv** : 2610.33826,10-24 v1,11-04 v2

## xingjian-bai/opal (OPAL, Reactive Optimizers) research report

> Channel: GitHub. Research day: 2026-11-06. Data card: repo created 2026-10-21, today 345 stars, 21 forks, +7 over the last 102 days, +7 over the first 98 days, single-day peak +58 (11-04), latest release v0.3.10.

### One line

OPAL is a research project from Xingjian Bai, an MIT CSAIL PhD student (advisors Vincent Sitzmann, Kaiming He, and Phillip Isola), with co-authors Jia-Hao Liang, Jayden Teoh, and Vincent Sitzmann (blog and arXiv 2610.33826). It ships a PyTorch package: a single line, `opal.wrap(opt, model, ...)`, wraps your optimizer. Every so often during training, Claude Code reads the training stats and rewrites two small programs (`updater.py` for the update rule and `analysis.py` for custom probes). In other words, a coding agent edits the optimizer's source code live, partway through a single training run.

**Verdict: it's a research codebase driven by an academic release event, not a product that grew on its own.** The stars came in three waves: 10-21 the author's X long post (218k views) plus the professor's reshare and an X Grok Trending topic; 10-22 a long-form explainer from Robert Lange (Sakana); and 11-02 official posts from MIT CSAIL's LinkedIn and Facebook accounts (LinkedIn: 1033 reactions, 34 comments), which drove a second wave of 11-03~04 (+14 and +58 stars). The differentiator (an agent changing optimizer code in the training "inner loop" rather than tuning from the outside) is why it got reshared, but actual adoption is tiny: under a thousand PyPI downloads in two weeks.

### 1. Data (GitHub repo / PyPI)

| Date | stars | Daily adds | Event |
|---|---|---|---|
| 10-21 | 0 | — | Repo created, blog live, author's X long post, HN post with 32 points |
| 10-22 | ? | — | Robert Lange X long post; 310 PyPI downloads that day |
| 10-23 | 145 | — | About 145 stars in the first two days (our tracking starts from this day) |
| 10-24 | 181 | +36 | arXiv v1 submitted (10-24); media and aggregators keep picking it up |
| 10-25~11-02 | 187→255 | 1–21 per day | long tail |
| 11-03 | 269 | +14 | 11-02 official MIT CSAIL LinkedIn/Facebook posts; 132 PyPI downloads that day |
| 11-04 | 327 | +58 | arXiv v2 (11-04); CSAIL posts keep the buzz going |
| 11-05~06 | 339→345 | +12, +6 | Decline |

Source: our GitHub repo_day data (2026-11-06); PyPI downloads from pypistats.org for `opal-optimizer` (mirrors excluded): 10-22 to 11-05 totaled about 976 downloads, 236 last week, 10 yesterday. The download curve tracks the star spikes exactly (10-22, 10-29, 11-03 peaks), which suggests "saw the news, pip installed it once," not sustained use. open_issues=0 and forks 21 also show no real user base asking questions yet.

The author's other repo on GitHub with stars is sparse-causal-diffusion (55 stars, code for a CVPR 2026 paper). The author has 37 followers on GitHub. The growth didn't come from the author's GitHub following; it came from academic X and the MIT brand.

### 2. Competitors / similar

Many people work on "getting AI to tune training automatically," but most do it from the outside (run many times, search configs). OPAL edits code from inside a single training run:

| Name | Form | Launched | Scale | How OPAL differs |
|---|---|---|---|---|
| karpathy/autoresearch | Open source | 2026-03-06 | 99,305 stars (our repo 11-06) | An agent repeatedly edits nanochat's training code in an outer loop, runs experiments, and compares results. OPAL only edits the optimizer, live within one training run, with no re-runs. |
| Sakana AI Scientist / v2, ShinkaEvolve | Open source | 2024-08 / 2025-04 / 2025-09 | 14,949 / 7,444 / 1,499 stars | Fully automated research / program evolution with many evaluations in an outer loop; not a wrapper inserted into the training loop |
| OpenEvolve (open-source AlphaEvolve reproduction) / Google AlphaEvolve | Open source / big tech | 2025-05 | 7,717 stars | Evolutionary search over algorithm code, also spanning many runs |
| Google VeLO / learned_optimization | Open source (learned optimizers) | 2021–2022 | 816 stars | Meta-training at scale bakes the optimizer into network weights; OPAL is zero-shot, with meta-knowledge from LLM pretraining (Robert Lange's comparison) |
| Lion (optimizer discovered by Google's symbolic search) | Paper / big lab | 2023 | Widely adopted | Searches offline for one fixed optimizer; OPAL keeps rewriting it during the run |
| Schedule-Free, Prodigy, D-Adaptation | open-source optimizers | 2023–2024 | 2,334 / 474 stars | adapt the learning rate with fixed hand-written rules; OPAL has the agent reason about it instead |
| PBT / Optuna / Ray Tune / W&B Sweeps | Open source / commercial | 2017– | Large scale | hyperparameter search needs many trials or multiple workers; OPAL needs a single training run plus LLM tokens |
| WecoAI/aideml | Open source / commercial | 2024-04 | 1,611 stars | an ML engineering agent that writes the whole outer-loop code |

Searching our GitHub repo index for repos created in the last 60 days whose descriptions mention "agent + optimizer / reactive optimizer / LLM tuning the learning rate": apart from OPAL, there are no repos with the same concept (the closest is bertaye/agentic-cuda-optimizer, 37 stars, which optimizes CUDA kernels, a different thing).

### 3. Growth tactics (evidence)

1. **Author's X long post (confirmed)** : @SimulatedAnneal 10-21 posted a 9-tweet thread + 24-second animation, 217.8K views, 1.5K likes, 273 reposts, 1.2K bookmarks (https://x.com/SimulatedAnneal/status/2112936831398375800).

2. **Advisor endorsement + X Grok Trending topic (confirmed)** : Vincent Sitzmann (MIT associate professor) reposted it the same day; X generated the trending topic "MIT's OPAL Lets AI Rewrite Optimizers During Neural Network Training" (https://x.com/i/trending/2112982610112926003).

3. **Domain account breakdown (confirmed)** : Robert Lange (Sakana AI) 10-22 long post comparing it with Luke Metz's VeLO learned optimizer (https://x.com/RobertTLange/status/2113352327042117692).

4. **Official MIT CSAIL account pushes it (confirmed)** : 11-02 CSAIL LinkedIn (184K followers, 1033 reactions / 34 comments) and a Facebook video; this lines up with the 11-03~04 star and PyPI download second wave. Co-author Jayden Teoh also posted on LinkedIn.

5. **AI news aggregator / automated news sites (confirmed)** : aiheadlinehub (10-23), glonce, AISignal, ai-dailynews, TheNextGenTechInsider, AGI Hunt, airebao (Chinese), iitog (Russian), arxivdaily (Chinese), and others, mostly 10-23~11-04 rewrites; YouTube Shorts and Instagram reposts too.

6. **HN (confirmed, but small)** : 10-21 "Reactive Optimizers: rewrite the optimizer's source code during training" got 32 points and 3 comments, never reached the front page (our hackernews index).

7. **arXiv / alphaXiv (confirmed)** : 10-24 submission, 11-04 v2, an academic traffic entry point.

8. **In-product "have your agent install it" prompt (confirmed)** : the README's top section gives an install prompt you can copy into Claude Code / Codex / Cursor, lowering the trial barrier; this is a conversion tactic, not an acquisition one.

No evidence found of ads, influencer promotion, or a Product Hunt launch.

### 4. Why it took off (really: why it got seen)

- **Author + lab brand** : MIT CSAIL, Sitzmann credited, author is a student connected to Kaiming He's group; CSAIL's official account pushed it directly, and academic circles on X distribute it by default.

- **The concept is "new" and explainable in one line** : "the optimizer thinks for itself and rewrites its own source during training," layered on the "Claude Opus as optimizer" topic, which fits X and Grok trending naturally.

- **Results with numbers worth sharing** : SiT-B/2 FID 35.7→23.6, LightningDiT-B/1 3.22→2.31, modded-nanogpt record #36 slightly exceeded, and it openly reports a failed run (3.2878), which earns both credibility and discussion.

- **Timing** : in 2026, karpathy/autoresearch (nearly 100K stars) made "agents doing ML research automatically" a hot topic; OPAL is a new variant of the "inner loop" on that line. Around the same time, another MIT CSAIL project on weight-space programming (GPT-6 Astra editing weights) was also trending during 11-02~03 (52.8K posts on X), so the MIT "AI rewriting AI" narrative was exposed all at once.

- **Engineering completeness above a typical paper's code** : a PyPI package, `opal doctor / estimate / report / compare` CLI, a `backend="keep"` bit-consistent dry run, cost caps, DDP, and a mailbox relay for GPU machines without internet, iterated to v0.3.10 in two weeks, so "read it, then try it right away" actually works.

### 5. Differentiation, and whether it was the reason for the growth

Differentiation: **the agent works inside a single training run's inner loop and modifies the optimizer through code** (per-param-group update rules, mid-run learning rate increases, swapping in Muon locally, bf16/fp32, momentum resets, masking bad batches), with zero meta-training and zero cross-run memory. Compared with autoresearch / AI Scientist / hyperparameter search, which all run in an outer loop over multiple runs, and with VeLO, which needs massive meta-training, this is the reason Robert Lange and others reposted it, so **the differentiation is why it's being discussed** . But its star count (345) and download volume (about a thousand) show it has only spread within ML research circles and has not yet "taken off." The practical barrier is also high: you need a GPU training job, a Claude Code account, tolerance for waiting on training while the agent decides (about 40 seconds per decision), and token costs.

### 6. Visibility and copycats

- Visibility: it's circulating in a small circle. ML academic X (the launch post with 218K views, Grok trending, Robert Lange), the MIT CSAIL official account, and dozens of AI news sites have covered it, but it is not a commercial product, has no revenue, and hasn't entered mainstream media (no formal MIT News article found, unverified).

- Copycats: no new repo with the same concept in our GitHub index over the last 60 days; no copying found.

### 7. Takeaways

- Launching an academic result as a product: an interactive blog animation + an X thread + a pip-installable package + a "copy this to your coding agent" install prompt, turning "reading the paper" directly into "trying it."

- Embedding an agent into one replaceable module of an existing algorithm (here `torch.optim`), with a two-snippet interface, rather than having the agent run the whole pipeline. This pattern can be carried over to other "fixed-rule system components" (schedulers, cache policies, data sampling, compiler flags).

- Designed for a sense of safety: bit-consistent dry runs, cost caps, and `opal estimate` reports time and money up front, lowering the psychological barrier for a first try.

### 8. Uncertain

- Real usage: about 976 PyPI downloads (including CI and curious installs), no issues filed, and perhaps only dozens of people use it in real training; unverified.

- Result reproducibility: most experiments were run only once, and the agent may make different decisions each time (per the author).

- The +58 on 11-04 is mainly attributable to the CSAIL official push and arXiv v2, both of which landed on those two days; their individual shares can't be separated.

- Star counts for the first two days (10-21~22) have no daily data; we only know 10-23 had 145.

### Sources

- GitHub README:[github.com/xingjian-bai/opal](https://github.com/xingjian-bai/opal)

- Blog: [xingjianbai.com/reactive-optimizers/](https://xingjianbai.com/reactive-optimizers/); arXiv: [arxiv.org/abs/2610.33826](https://arxiv.org/abs/2610.33826)

- Author homepage: [xingjianbai.com/](https://xingjianbai.com/)

- X:[x.com/SimulatedAnneal/status/2112936831398375800](https://x.com/SimulatedAnneal/status/2112936831398375800) ;[x.com/i/trending/2112982610112926003](https://x.com/i/trending/2112982610112926003) ;[x.com/RobertTLange/status/2113352327042117692](https://x.com/RobertTLange/status/2113352327042117692)

- MIT CSAIL LinkedIn:[www.linkedin.com/posts/mit-csail_what-if-an-optimizer-could-…](https://www.linkedin.com/posts/mit-csail_what-if-an-optimizer-could-think-react-activity-7523067299860295680-zbDf)

- Media: [aiheadlinehub.com/2026/10/23/opal-agent-rewrites-optimizer-d…](https://aiheadlinehub.com/2026/10/23/opal-agent-rewrites-optimizer-during-training/); [www.thenextgentechinsider.com/pulse/mit-researchers-introduc…](https://www.thenextgentechinsider.com/pulse/mit-researchers-introduce-opal-for-real-time-training-optimization-via-llm); [airebao.com/item/52025](https://airebao.com/item/52025)

- PyPI downloads: [pypistats.org/packages/opal-optimizer](https://pypistats.org/packages/opal-optimizer)

- Our repo index: GitHub repo_day / repo_profile, hackernews story (2026-11-06 lookup)

This article was written by AI from public data and the data we collect daily. Numbers are sourced, and inferences are labeled.