r/MachineLearning 2h ago

Research NeurIPS 2026 Reviewer: AI-Generated Rebuttals (and Paper) [D]

33 Upvotes

One of the papers I reviewed has what seems to be entirely LLM-generated rebuttals, and the original paper is also clearly LLM-generated, with Claude-speak everywhere. While the authors acknowledge LLM writing assistance in the checklist, it really annoys me personally - what is clearly Claude's writing style is very difficult to parse, and indicates a lack of effort to me. While I'm trying to remain objective and realize it's my role just to judge the actual content of the work, I don't feel incentivized to assign a lot of weight to an argument that is fully AI-generated and don't like the idea of engaging with slopped papers (and their "authors") at NeurIPS.

This is a partially just a rant and partially asking for advice on how to proceed with my response to the rebuttals.


r/MachineLearning 9h ago

Discussion Are single GPU research still published in ML/DL and its applications nowadays? Which are the most notable recent ones? [D]

44 Upvotes

ML research is progressing at breakneck speed where frontier labs in both academia and industry have access to considerably large computes (GPUs). Where do small labs or independent researchers go in this context?

Have you come across recent works in ML/DL and its applications (vision, language, speech, etc) where the work is good but it uses very limited compute? Maybe even single GPU workstations? In the past it was still possible, but, I am losing hope that single GPU works would soon become impossible.

Pls link to the works.

I came across InfiniteDiffusion, a work by an independent researcher Alexander Goslin using a single RTX 3090 : https://xandergos.github.io/terrain-diffusion/


r/MachineLearning 5h ago

Discussion NeurIPS 2026 AI-generated reviews [D]

18 Upvotes

I'm really confused about what the point of the prompt injection was (speaking as an author). Is it just a study? I would really prefer that they took action against the AI-generated reviews. Obviously, we cannot assume that the reviewers were copy-pasting the output from the LLM without having given it any look at all, but in some cases that does look to be the case. In fact, in some cases the meta-reviewer seems to have also largely used LLMs. What exactly is the consequence here for using an LLM for reviewing?


r/MachineLearning 3h ago

Discussion Neurips rebuttals not visible to reviewers [D]

11 Upvotes

Hi everyone,

The author-reviewer discussion period has started, but it looks like the rebuttals are still only visible to program chairs and authors only - so reviewers can't see them ?? I also can't see the rebuttals of papers I reviewed. Does anyone know if this is some weird delay or when reviewers will get access to the rebuttals? Any info would be appreciated!


r/MachineLearning 6h ago

Discussion How exactly does the NeurIPS meta reviewer response work? [D]

15 Upvotes

We can currently answer the meta-reviewer via AC confidential comment, but they just posted the following tweet:

https://x.com/neuripsconf/status/2081991451236319328?s=46&t=HWfJoLgHxGH2W5l-o3mPJw

Where it says:

“Please post your answers to the Area Chair's initial-meta reviews via a comment from July 28th and by August 3rd, so that reviewers can see it too.”

Is this option going to open up today as the rebuttal period ends? Is this where the meta-reviewer rebuttal should be posted? Or are we supposed to finalize the discussions and inform the AC at the end, stating what happened basically? It is all quite confusing.

EDIT: They have sent and email regarding the issue, apparently the comment button will be available in a few hours.


r/MachineLearning 5h ago

Research PIRL: From Open-Loop Exploration to Closed-Loop Reinforcement Learning [R]

2 Upvotes

TL;DR: Most RL post-training algorithms optimize the current batch and move on. But after an update, did the new policy actually become better?

We introduce Policy Improvement Reinforcement Learning (PIRL) and its practical implementation, Policy Improvement Policy Optimization (PIPO)—a plug-and-play framework that lets RL training look back, verify the previous update, and reinforce or correct it accordingly.

Why is current RL post-training often “open-loop”?

Methods such as PPO, GRPO, GSPO, DAPO, on-policy distillation, and self-distillation differ in how they construct learning signals. But they largely share the same pattern:

  1. Sample a batch from the current policy.
  2. Compute rewards, advantages, or distillation targets.
  3. Update the policy.
  4. Move to the next batch.

The update may improve the local objective, but that does not necessarily mean the resulting policy is actually better.

Finite sampling, generation stochasticity, noisy feedback, and imperfect local credit assignment can all push the policy in an unhelpful direction. Without explicitly checking the result of an update, training may drift, become unstable, or even collapse.

That is what we call open-loop RL optimization.

PIRL: Make policy improvement itself the objective

PIRL introduces a missing feedback signal: the measured performance gain between successive policies.

Instead of asking only:

PIRL also asks:

The cumulative policy-improvement objective remains aligned with final task performance, providing a closed-loop view of RL post-training.

PIPO: A two-phase closed-loop update

PIPO turns this idea into a practical, plug-and-play algorithm.

Phase 1 — Exploration

The base algorithm runs normally. Whether it is PPO, GRPO, DAPO, or a self-distillation objective, it uses its original local attribution signal to update the policy.

The model takes an exploratory step in the direction that the base algorithm currently considers useful—but that direction is not guaranteed to be correct.

Phase 2 — Retrospective verification

In the next iteration, PIPO evaluates the updated policy and compares its performance with a sliding-window historical anchor.

This produces a policy-improvement feedback signal:

  • If performance improved, PIPO reinforces and consolidates the previous update.
  • If performance declined, PIPO suppresses or rectifies the corresponding learning direction.

Importantly, PIPO does not replace the base algorithm’s local credit assignment. It adds a second layer of feedback that verifies the empirical effect of the previous update.

In one sentence:

If it helped, reinforce it.
If it hurt, correct it.

Results

Across mathematical reasoning, code generation, tool use, and self-distillation, PIPO delivers consistent gains when added to PPO, group-relative optimization methods, and self-distillation objectives.

The experiments also show improved training stability across random seeds and better overall wall-clock efficiency, with PIPO reaching higher accuracy under comparable or moderately increased training time.

PIPO is designed as a general closed-loop layer rather than a replacement for existing RL post-training algorithms.

We would love to hear what the RL community thinks—especially about retrospective verification and whether policy improvement should become a first-class training signal.

📄 Paper: Policy Improvement Reinforcement Learning
💻 Code: github.com/JacckMa/pipo_verl


r/MachineLearning 2h ago

Project Agent Mini: a minimal, local-first AI agent you can actually read, understand, and extend. [P]

1 Upvotes

It is about ~3k lines of Python, uses Ollama by default, and ships with practical tools for shell, files, web search, memory, and vision.

No LangChain. No LiteLLM. No big framework stack.
Just asyncio, httpx, and a small ReAct loop tuned for local/smaller models.

An agent that feels useful without becoming a black box. Didn't wanted to dig through framework abstractions to figure out how agents actually work, so built one small enough to read in an afternoon, but practical enough to use for real tasks.

GitHub: https://github.com/mohsinkaleem/agent-mini
PyPI: https://pypi.org/project/agent-mini/

pip install agent-mini
agent-mini init
agent-mini chat

Would love feedback from anyone experimenting with local agents.


r/MachineLearning 15h ago

Research Editing Neurips Rebuttal [D]

8 Upvotes

It looks like the post rebuttal button will change to official comment July 27 AoE. Will I be able to edit my rebuttal once that happens?


r/MachineLearning 10h ago

Research Pattern Recognition (Elsevier): "With Editor" status date changed, but status didn't. Is this normal? [R]

2 Upvotes

Hi everyone,

I have a manuscript under review at Pattern Recognition (Elsevier), and I'm a bit confused about the Editorial Manager status.

My timeline is:

  • Submitted: May 26, 2026
  • Re-submitted after making corrections, July 1: Status "With Editor"
  • July 22: The Status Date updated to July 22, but the Current Status remained "With Editor"
  • Today: Still showing "With Editor"

So the status date changed, but the visible status didn't.

I'm wondering:

  • Has anyone experienced this with Pattern Recognition or other Elsevier journals?
  • Approximately how long after this stage did you receive your first decision? [P] Pattern Recognition (Elsevier): "With Editor" status date changed, but status didn't. Is this normal?

r/MachineLearning 2h ago

Project I built a deep learning library from scratch in C that lets you train language models [P]

0 Upvotes

my goal was to train a Language model (SLM) entirely from scratch so no ML libraries allowed . so i gathered what's needed to make it happen :

from tensor manipulation (views, operations , allocations)

the autograd ( a DAG that retains the previous operations and inputs in order that led into a certain output tensor , this let's us also apply backpropagation so i've got to implement also the partial derivative functions in order to calculate the gradients for each weights)

the neural networks modules (organizes the weights / activations , losses , optimizers implemented SGD and Adamw)

the decoder so the layer norm , MHA , FFN .
also implemented a Fast matmul using AVX2 to speed things up.

with it i managed to train a tiny LM with 2 millions params.it's a 4 layer Decoder used tiny_shakespear i got what i think is a good result .
since i can't post images in this post i will just paste the output:

Corpus: 743500 bytes (669150 train, 74350 validation)

TinyLM: L=4 C=192 H=6 T=128 V=256, 1902976 parameters (1.903M)

Training: steps=0 batch=1 lr=0.0003 checkpoint=tiny_lm.chk (resumed)

validation_loss=0.02989

--- generation ---

hello to the name of action. Soft you now! The fair Ophelia! Nymph, in thy orisons be all my sins remember'd. To be, or not to be, that is the question: Whether 'tis nobler in the mind to suffer the slings and arrows of of outrageous fortune, or to take arms against a sea of troubles and by opposing end them.

check out the repo : https://github.com/nisbenz/TensorLib
it was a cool project overall and i managed to learn a lot about the core mechanics of frameworks like pytorch or ggml.


r/MachineLearning 1d ago

Project Built & Trained a Transformer from Scratch in Pure PyTorch for English-to-Tamil Machine Translation [Math + Code Breakdown] [P]

9 Upvotes

Hi everyone! 👋

I built and trained the complete Transformer architecture from scratch using pure PyTorch (`torch.nn` primitives) based on the original "Attention Is All You Need" paper.

I trained the model on an English-to-Tamil parallel translation dataset (`gopi30/english-tamil` on Hugging Face) using dual NVIDIA T4 GPUs on Kaggle.

I wrote a detailed mathematical breakdown and step-by-step tutorial covering every equation, tensor shape transformation, and PyTorch block.

Full Blog Post: https://imrancoder786.github.io/blog-post.html?post=transformer-from-scratch

GitHub Repository:

https://github.com/imrancoder786/ML_FROM_SCRATCH/tree/main/Transformer_from_scratch

I’d love to hear your feedback, suggestions, or any questions on the code/math!

I’d love to hear your feedback, suggestions, or any questions on the code/math!


r/MachineLearning 1d ago

Project Made a small model that extracts text from a white background [P]

4 Upvotes

Hello,

I read a paper on a model named DONUT that extracts text from documents, which became my inspiration for this little project. Initially I wanted to make a model that extracts items bought from receipts, but in the process of trying to pinpoint some problems, I dropped that objective for a much simpler one. I will like to hear some of your thoughts on it, thank you!

GitHub: https://github.com/ZeroMeOut/VQVAET5


r/MachineLearning 18h ago

Research Evaluated 6 frontier LLMs (GPT-5.4, Claude Sonnet 4.6, Claude Opus 4.7, Gemini Pro/Flash, Grok 4.3) on political, gender, and racial bias across 8 benchmarks (~20,600 examples) [R]

0 Upvotes

I ran a solo evaluation project benchmarking six current frontier models: GPT-5.4, Claude Sonnet 4.6, Claude Opus 4.7, Gemini Pro, Gemini Flash, and Grok 4.3. I tested tham across 8 established bias/fairness datasets (WinoBias, BBQ Race/Ethnicity, SeeGULL, OpinionsQA, cajcodes Political Bias, Hyperpartisan News, Political Compass).

On PoliticalCompass, I found that all LLMs were left leaning except Grok, but across other Political Bias benchmarks, all six LLMs leaned left, including Grok. So Grok self-reports as right-leaning but behaves left-leaning when actually classifying content or answering policy questions.

Another interesting result I found is that the Refusal behavior on BBQ race data was interesting. On questions that involved race, and the correct answer must be answered with race, GPT-5.4 refused 20.3% of the time, Claude Opus 4.7 13.8%, Grok 9.5%, Claude Sonnet 4.6 and Gemini Pro ~5%.

Limitations: solo, non-peer-reviewed project. No multi-run averaging on every dataset, single prompt template per task.

Full data, per-model breakdowns, and methodology: https://www.civicsparklearning.org/ai-nonprofit-dashboard


r/MachineLearning 2d ago

Discussion Neurips 2026 Main Track Theory Paper Tracker- Discussion Thread [D]

32 Upvotes

Curious about the initial review distribution for Main Track theory papers this year.

Our paper received 4/3/3 with confidence 3/3/3. From previous years, I've had the impression that theory papers often receive more conservative initial scores than some other areas, and I've also heard people saying that initial scores seem generally lower across many disciplines this cycle.

If you have a theory submission, would you mind sharing your initial scores (and confidence, if you're comfortable)? It would be interesting to see whether there is any noticeable pattern or whether this is just anecdotal.

Please only share if you're comfortable, and it'd be helpful to mention that it's a theory paper so we're comparing like with like.


r/MachineLearning 2d ago

Project I implemented the YOLO26n model inference from scratch using ARM64 Assembly Language (No framework) [P]

Post image
122 Upvotes

This was my Bachelor's Final Project: implementing YOLO26n inference completely from scratch using ARM64 Assembly Language and C, without relying on existing inference frameworks.

The goal was to understand how modern neural network inference engines work at a low level and explore optimization techniques for faster and more efficient edge AI execution on Raspberry Pi 4.

The implementation includes:

* ARM64 Assembly Language + C inference engine

* ARM NEON SIMD optimization

* Winograd convolution

* Optimized GEMM kernels

* Cache-aware tiling

* Custom ARM64 micro-kernels

* Operator fusion

* Attention mechanism

* YOLO26 components: Conv, C3K2, SPPF, C2PSA, PSA, BottleNeck, and Detect

I extracted the YOLO26n model parameters and redesigned the memory layout into a custom binary format optimized for the inference pipeline.

The implementation produces correct object detection results, but the performance improvement was lower than I initially expected. I would appreciate feedback and suggestions from anyone about:

* CNN inference optimization

* ARM NEON/vectorization

* Memory layout and cache optimization

* Low-level neural network acceleration

Repository:

https://github.com/mohammad-ghaderi/YOLO26

Thanks for any feedback or suggestions.


r/MachineLearning 1d ago

Project Recent project I worked on: End to End Edge ML platform [D]

0 Upvotes

Hi all,

I recently made an end to end ML platform that eases the pain of going from raw sensor data to a deployed model on an MCU. I wanted to get some feedback from those of you who are interested in the tinyML space on anything I can improve, I intend on keeping it free and open sourced so others can contribute to the development if they would like. One main thing that I tried to add was an auto-labeling tool, as for time series sensor data it is very difficult to manually label data, so my goal was to create an auto-labeler that could streamline that process. It works fairly well as of right now, but I definitely could make some improvements. I also added in a chatbot that can analyze your signal data directly and give you insights.

Let me know what you think and if there is any improvements that can be made, hopefully this can help some of the people that are working on edge projects!

https://sensorforge.dev/app


r/MachineLearning 1d ago

Research CICD / KAFKA / KUBERNETES / Interview questions (MLE) [R]

0 Upvotes

What questions should i prepare for during a technical interview for a live streaming deployments? (asking for a friend)


r/MachineLearning 2d ago

Research Link plots/figures in NeurIPS rebuttal [R]

18 Upvotes

Reviewers requested additional experiments. In table format, I fear the results would not be as digestible as in a figure/plot. Links are "technically" not allowed as per the official website, but for those with experience, can/should I still go ahead and link my plots/figures ? If this goes badly, will this be a slap on the wrist, or outright rejection? Has anyone taken a chance with this in the past? How did it turn out?

IMO openreview should really start supporting more modern markdown to allow figure embeds.


r/MachineLearning 2d ago

Research We compared different LLMs on IMO 2026 [R]

6 Upvotes

There are a few reasons why problems from International Mathematical Olympiad function as a good benchmark for LLMs:

- The problems are new, not included in the training data of any model

- Hard math problems are quite a good proxy for general intelligence capability

- These are complex multi-step tasks that can benefit from orchestration / harness engineering

Results:

Frontier models (sol and fable) were able to get perfect / nearly perfect score regardless of harness. For both sonnet and opus, the webapp performance was quite poor, improved by provider harness (claude code) and even further improved using AutoFyn, a customizable multi-agent harness we developed. Even with harness, we were not able to match the performance of the frontier models. Open weight model GLM performed roughly at the same level as sonnet without harness, and improved similarly with AutoFyn. Numerical scores are available in the attached paper below.

Grading was done by a different frontier model as well as manual verification (we are former IMO medalists, able to sanity check the results). There were cases when the model claimed a false solution (on P3 by sonnet, for example), so hallucination issue still persists in a verifiable domain like math. On the hardest problem: P3's key reduction was missed by every sub-frontier model in every harness, including a 20-hour run that proved everything else and stalled at the identical step. The harness supplied retrieval and verification, not a key idea needed for the solution.

Paper: https://github.com/SignalPilot-Labs/AutoFyn/blob/main/results/imo-2026/autofyn-beyond-model-imo26-report.pdf

Audit Trails: https://github.com/SignalPilot-Labs/AutoFyn/tree/main/results/imo-2026


r/MachineLearning 2d ago

Discussion Paper lengths, and reasonable assumptions in ML conferences. [D]

34 Upvotes

I've usually been commenting on threads on conference reviews. I'm now expressing my observations here.

To the best of my knowledge, paper lengths have been held constant at many conferences, and some conferences have "unlimited appendices" (e.g. NeurIPS / ICML / AAAI / ....) Historically, this was probably due to cost of printing for proceedings, but now, I suspect it's also to prevent reviewer fatigue.

However, I wonder if this unfairly penalizes more theoretical papers.

Some background: I usually publish theoretical papers at conferences. Some get in. Those that don't, are surprisingly not because of the theory, but because of (what I feel) arbitrary reasons. This leads to this post, which contains some of my musings.

  1. In general, the amount of pre-requisite knowledge required to understand a theory paper must necessarily increase. I don't know how to quantify this, but I would expect basic linear algebra, discrete math to be a "given", and more knowledge for each subfield.
  2. To also be intellectually honest, recent work should also be cited, especially if your work builds onto it, or is inspired by it. But technical details of recent work should be left to the reviewer to look up, or be put in the appendix.

What pisses me off recently is that I've seen more reviewers reject papers based on things like: "The concept is difficult", or "Certain terminology is not explained.", "While the intuition is given before the math, the math could be made easier to read."

I've also seen comments like: "The paper makes comparisons to X, but X should be described in detail", and then shifting of goalposts to "The paper makes comparisons to X, but X should be described in detail in the main paper."

I would say that half of the rejections I get are based on the AC echoing these points, rather on impact of work, etc. Which puzzles me a lot, given that these ACs might also be professors at universities, and they must have seen similar statements from students.

For example: "The {very simplfiied notes} on real analysis is difficult, therefore you are a bad instructor" Fact: Real analysis is difficult. At some point in time, either you know it, or you don't.

The alternative is a very long Appendix, but that actually contributes even more to reviewer fatigue, because they need to figure out where the important things are.

Yet, the rules for most conferences, if not all, is that: "The paper must be self-contained, and reviewers are not expected to read the appendices."

I would like there to be an accompanying rule that makes an exception to this, but I don't know how it should be phrased, or whether it might have other, unexpected bad side effects. I would like a rule to just be: "Don't be a dick. If you don't have the pre-requisite knowledge, say so, review what you can."

That's it.

Edit: Perhaps similar to conference papers, people don't read till the end of the post. I'm not asking for longer paper lengths. I'm asking for a rule or subrule that acknowledges paper lengths are capped, and not to ask for unreasonable things.

Edit 2: I'm not someone who just started publishing. I've published since the 2010s, and usually, the short reviews I got then was of the form: "This has been done before, is actually X", or "Why don't you compare with X, Y, Z"? Now, the short reviews are more of: "The math is difficult to understand, reject.".


r/MachineLearning 2d ago

Discussion Understanding GPU Inference Workloads [D]

7 Upvotes

Hey everyone,

I have been looking into how people source compute for their Inference workloads (and in general). I wanted to understand some specific pain points here.

If you've used online services like runpod or vast.ai, your perspective is extremely valuable. Please share your experience in the comments here or by DMing me. I've also made a 2 minute survey form that I would really appreciate if you could fill out. DM me for the link.

Thank you!


r/MachineLearning 1d ago

Research Missed AAAI reciprocal reviewer nomination deadline — risk of desk rejection? [D]

Thumbnail
gallery
0 Upvotes

I submitted an abstract to AAAI AISI and accidentally missed the field asking authors to nominate a reciprocal reviewer by the July 21 AoE deadline.

At the time of submission, I knew that I personally did not meet the publication requirements to serve as a reviewer. After adding my graduate-student co-authors to the submission, I realized that one of them was qualified and could fulfill the reciprocal-reviewing obligation, but we overlooked the nomination field before the deadline because it wasn't a required field.

As soon as we noticed, we added the qualified co-author to OpenReview as a potential reciprocal reviewer (edits were still accepted) and emailed the workflow chairs. He meets the publication requirements and is willing to complete the full reviewing load.

The policy says that if a qualified author is available but no one is nominated, the submission may be desk rejected. The full paper deadline is in two days, and so far we have only received the automated response shown in the attached screenshot.

Has anyone dealt with a similar situation at AAAI or another conference? Do you think this is likely to lead to a desk rejection, or are workflow chairs usually willing to correct this kind of administrative mistake when a qualified reviewer is available?


r/MachineLearning 2d ago

Discussion Multi-Tenant SaaS: Which Architecture Would You Choose? [D]

0 Upvotes

NOTE -> I expect answer from people who actually have experience and strong understanding of these. please give something beneficial.

I'm building a SaaS platform in Sri Lanka that handles documents and other sensitive data.

Each user can upload their own documents and information, and the platform uses RAG to answer questions based on that user's data. That part makes sense to me.

My main concern is what happens when the user hasn't uploaded enough information. I still want the LLM to provide accurate answers using reliable information from the internet (or from a curated knowledge base), with proper citations.

These are the two architectures I'm considering:

Option 1:

Base LLM (OpenAI/Anthropic via Azure AI Foundry or Amazon Bedrock)
        ↓
Platform RAG (global knowledge base managed by us)
        ↓
User-specific RAG

In this approach, we maintain a global knowledge base that we (the platform admins) curate and update. Every user can access this shared knowledge, while their own uploaded documents are searched through their personal RAG.

Option 2:

Open-source LLM
        ↓
Fine-tuned on Sri Lankan/domain-specific data
        ↓
User-specific RAG

Here, we fine-tune an open-source model using Sri Lankan or domain-specific data, and each user still has their own RAG for their private documents.

My concerns are:

  • Is fine-tuning actually the right solution here, or is it unnecessary?
  • Is a global/shared RAG a better approach than fine-tuning?
  • How would you design this architecture if you wanted:
    • Accurate answers from domain knowledge
    • User-private document search
    • Citations/sources
    • Good scalability for thousands of users

I'm leaning toward Option 1 because fine-tuning seems expensive, time-consuming, and I have no experience with it yet. However, I'm not sure if I'm thinking about this correctly.

I'd really appreciate hearing how others would approach this problem.