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salma-remyx

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https://remyx.ai

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reacted to breitburg's post with 🔥 1 day ago

I've been experimenting with "pure" model alignment. The core idea is to only train a verifiable version of a capacity until the model generalizes it to the non-verifiable version. For example, training the model on factual self-knowledge, like the model's scale, architecture, runtime situation, and being able to predict its own behavior, betting this generalizes to real introspection about states that do not. The same principle applies to general instruction following -- no training on subjective judgement, only verifiable claims and inferences, betting the skill generalizes to instructions where correctness is a matter of judgment. The primary alignment claim is that an identity and taste that will emerge this way will be much more robust and honest than hand-scripted ones (e.g. "As an AI language model..."). During the training, we should never teach it to make any subjective claims or invent experiences that we assume it has, like "I don't have taste" or "I'm not self-aware in the way you think", as well as no narration of internal states like "I'm curious now". The main threat, of course, is that we'll simply inherit the training distribution of all the things like "taste", and we'll get an average. However, with the recent research about the models' introspection abilities, it might be as well the case that we'll get something that's more honest than something that tries to adhere to a specific spec file. I'm posting new experimental models trained that way in this collection: https://huggingface.co/collections/breitburg/neue

posted an update 1 day ago

What's holding your code back? Outrider finds, implements, and validates methods for your repo. While testing Outrider on a fork of huggingface/peft, I discovered "Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models" (arxiv: 2402.02347) The work offers improved stability and faster convergence in LoRA finetuning by adjusting updates for curvature that LoRA optimizers typically ignore. Not the most recent paper, so I was pleasantly surprised my action surfaced this method as a candidate before implementing a PR. Even more surprised this method had not already been merged upstream. Turns out, the author did try contributing to peft a couple years ago, but people get busy and the PR was closed after going stale. So I decided to revive it! I opened an issue and soon after the author engaged to help land the feature. Now huggingface/peft #3382 is open, a joint effort with the paper's author. This whole episode has me thinking about the future of OSS maintenance with AI coding. The software projects which endure will be well-shaped to quickly land and help test new ideas. Across 30 forks, I've seen several papers land as clean PRs for multiple repos, which offers a perspective on how methods impact applications. Recent methods matching multiple frameworks: STARE, Entity Binding, BINEVAL Get Outrider: https://github.com/remyxai/outrider

reacted to fffiloni's post with 🔥 2 days ago

⏱️ Built a small Space for Visual Chronometer / Pulse of Motion. Upload a video and estimate its Physical FPS: the frame rate implied by visual motion, independent of metadata. Useful to inspect “chronometric hallucination” in generated videos: clips that look smooth, but move with the wrong physical time scale. Try it here: https://huggingface.co/spaces/fffiloni/Pulse-of-Motion

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859

What's holding your code back?
Outrider finds, implements, and validates methods for your repo.

While testing Outrider on a fork of huggingface/peft, I discovered "Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models" (arxiv: 2402.02347)

The work offers improved stability and faster convergence in LoRA finetuning by adjusting updates for curvature that LoRA optimizers typically ignore.

Not the most recent paper, so I was pleasantly surprised my action surfaced this method as a candidate before implementing a PR. Even more surprised this method had not already been merged upstream.

Turns out, the author did try contributing to peft a couple years ago, but people get busy and the PR was closed after going stale.

So I decided to revive it! I opened an issue and soon after the author engaged to help land the feature. Now huggingface/peft #3382 is open, a joint effort with the paper's author.

This whole episode has me thinking about the future of OSS maintenance with AI coding. The software projects which endure will be well-shaped to quickly land and help test new ideas.

Across 30 forks, I've seen several papers land as clean PRs for multiple repos, which offers a perspective on how methods impact applications. Recent methods matching multiple frameworks: STARE, Entity Binding, BINEVAL

Get Outrider: https://github.com/remyxai/outrider

Post

5848

In that benchmark comparison, do you even have the sample size to distinguish two models, or are you making decisions based on statistical noise?

"Resolution Diagnostics for Paired LLM Evaluation" offers a simple check: a per-pair resolution ratio q = N/N* that flags when a displayed ranking sits below the resolution floor regardless of p-value.
arXiv: https://arxiv.org/abs/2605.30315v1

Outrider automatically matched this paper to our fork of lm-evaluation-harness and opened a PR implementing the diagnostic.

Configure the action to find new methods tailored to your repo: https://github.com/remyxai/outrider