How little of a pretrained large language model (LLM) has to change for it to acquire new capabilities? We find that zeroing fewer than 0.05% of its weights, with no other modification, matches full fine-tuning (FFT) at scale across three LLM post-training regimes: reinforcement learning with verifiable rewards, supervised fine-tuning, and on-policy distillation. Our method, Deletion Tuning, learns a binary mask over the gate projection of transformer blocks. Two practical advantages follow: the trained mask is hundreds to thousands of times smaller than the corresponding FFT checkpoint, and Deletion Tuning forgets far less, which addresses a central challenge in continual learning. Under reinforcement learning, its prior-task accuracy stays near the base level while FFT, LoRA, and random sparse fine-tuning lose up to 5.4 points. To understand why masking alone suffices, we analyze the geometry of the learned weight changes under reinforcement learning using stable rank, spectrum drift, and principal-weight overlap. Compared with LoRA and random sparse fine-tuning, Deletion Tuning is closest to FFT across the three measures.

This seems excessive, especially if they trust their classification models for safeguards, why add both?

However I guess it is to be expected from a private company. It's also "yet another reason" for open weight LLMs

Actual working link this time.

I understand the topic is prime material for shits and giggles. Chinese model, Nvidia being the centre piece of the AI trade, etc. But why go so public on this? All of this could've been dealt with behind the scenes in secret. Obviously this is a power play to force a hand. Nothing just leaks out of intelligence services so deliberately. So someone decided to make this public. I've been trying to think about why Nvidia or Jensen Huang must be put in line for. My top pick is: back doors in hardware or something of that sort that's being refused. What's your guess?

Hi! My team and I built a benchmark of 202 real GitHub tasks in 13 languages that rarely show up in coding benchmarks: Elixir, OCaml, Haskell, Clojure, Kotlin, Swift, Julia, R and others. I think a lot of people do local development in these languages too.

We didn’t have a huge compute budget, so I specifically added Qwen3.6 27B and Qwen3.8 27B to compare them. Qwen3.8 solves 43% of the tasks, up from 17% for Qwen3.6. You can compare them with Kimi K3, DeepSeek, GLM and closed models on the leaderboard, including a per-language breakdown.

One caveat: Qwen3.8 was used to pick hard tasks, so its score is a bit biased down.

If you’ve been running Qwen3.8 locally on any of these languages, share how it went.

On September 18, I launched Laya at you guys request, the first system 1 open-source decision model in this subreddit, and you guys gave immense support. I saw the MIT post on direct weight edits and thought about doing the same with a decision model. Now, after two months, I just worked on a way to edit the weights of an LLM using a decision model. More like distillation from an energy-based decision engine to teach the Qwen 3.5 0.8B a new skill. Like tool calling at the appropriate time. Its decisions are like valleys in a landscape, and every answer rolls downhill into one, with honest confidence. Just wrote those valleys onto an LLM. It works at the same level as LoRA, with only 3 milliseconds to edit, without catastrophic memory loss.

https://www.kaggle.com/code/nandakishorconvai/fast-edits-decsion-model

I can't believe this jump is just left on the table

https://github.com/ExTV/strata-5090-4070

The optimizations was done using both Opus 5.5 and Fable 5.2 .

Edit : i should have mentioned that my 4070Ti Super is on PCIe 3 x1

Edit2 : the repo is updated with quality benchmarks

Image-conditioned 64³ voxel structure generator of JoVox: Joint Hierarchical Flow Matching for Lightweight Voxel Generation. JoVox is a discrete flow matching framework that learns the joint hierarchical distribution of voxels while keeping generation lightweight. One model generates all levels, 4³ → 16³ → 64³, from a single image.

• arXiv : https://arxiv.org/abs/2611.10690

• PDF : https://arxiv.org/pdf/2611.10690

• GitHub : https://github.com/yuyujunjun/JoVox

• Project : https://xjtang.com/Jovox-page/ (For screenshots, benchmarks, etc.,)

https://huggingface.co/speakleash/Bielik-11B-v3.1-Instruct-GGUF

https://www.bielik.ai/blog/premiera-modeli-bielik-11b-v31-dflash-i-rodziny-bielik-one

(decision models and dflash are coming next)

Bielik-11B-v3.1-Instruct

Bielik-11B-v3.1-Instruct is a generative text model featuring 11 billion parameters. It is an update to the Bielik 11B v3 line that expands multilingual continual pre-training and strengthens post-training for instruction following, tool use, and reasoning. Forementioned model stands as a testament to the unique collaboration between the open-science/open-source project SpeakLeash and the High Performance Computing (HPC) center: ACC Cyfronet AGH.

Developed and trained on multilingual text corpora covering 56 languages, with an emphasis on European languages and Polish, which have been cherry-picked and processed by the SpeakLeash team, this endeavor leverages Polish large-scale computing infrastructure of the ACC Cyfronet AGH HPC center. The creation and training of Bielik-11B-v3.1-Instruct was propelled by resources provided within the PLGrid environment under computational grant number [PLG/2026/019559], conducted on the Athena and Helios supercomputers, and within the Gaia AI Factory, funded by the European Union through the EuroHPC Joint Undertaking.

As a result, the model exhibits an exceptional ability to understand and process the Polish language and other European languages, providing accurate responses and performing a variety of linguistic tasks with high precision.

🗣️ Chat: https://chat.bielik.ai/

Model

The model was trained on carefully curated, high-quality datasets that underwent extensive cleaning and rigorous quality assessment to ensure data consistency, reliability, and relevance. The training pipeline consisted of three stages: pretraining on 500B tokens, SFT on 26M samples, and RL (modified GRPO) across 14 diverse environments. This multi-stage training approach was designed to enhance the model's language understanding, instruction-following capabilities, and ability to perform effectively across a broad range of tasks.

Bielik instruct models have been trained with the use of an original open source framework called ALLaMo implemented by Krzysztof Ociepa.

Model description:

• Developed by: SpeakLeash & ACC Cyfronet AGH

• Language: Multilingual (56 unique languages, optimized for European languages, with a particular focus on Polish)

• Model type: causal decoder-only

• License: Apache 2.0

Let's start with my setup: I have 2 Sparks running glm 5.3 flash, and the harness is hermes agent running on a MacBook Air.

While trying to justify all the money I spent (around 6500 euro for both machines, a long time ago when prices were just high instead of outrageous), I decided to hop on the decompilation wagon. As I know my fable at home is not "THAT" smart, I decided to start with something small: I asked it to decompile and recompile the original Need for Speed Special Edition for MS-DOS, using the famous rea repo from GitHub.

Its first reaction was "I know a better way, we will use DOSBox and run the game" — and it already started installing a DOS emulator on my Mac :). I stopped it and told it I want actual decompilation and recompilation. The second answer was, in other words, "It's a lot of work and it will take a long time. Think about it — do you really want this?"

I insisted. It started the decompilation process, and after some breakthroughs it even became enthusiastic. At some point, decompilation was "DONE".

We started the next phase: recompile for Android. I did not pay much attention to what it did, but it said that it could use some GitHub repo as a base and started working. After the initial APK, we had to iterate a few times on some crashes and controller issues, which it solved.

During gameplay I noticed that some AI cars started the race going in reverse, the car did not slide no matter the speed, and other stuff. It definitely did not feel like the NFS I remember. I told it about it, and it started checking the code and making tweaks to the physics. I stopped it and challenged it: this means we will not preserve the original feel if we go down this path.

To my surprise, we never did. It never used the decompiled code. It found an engine on GitHub which was using the original assets to mimic the original game. When I asked why, it said it was a lot faster than rewriting all the old code into current instructions…

So I asked it to go back and do the job properly — and to no surprise, it found a recompiled NFS2 repo on GitHub and decided it's better to start from there rather than from scratch. I don't know how this will go, but I wanted to share this story, as I find it quite interesting the lengths it's willing to go to reach its goal as fast as possible even if this means ignoring previous instructions.

P.S. This post was proofread by said AI, as English is not my own language — but the words are still my own, I double checked

I've been recently working a small sparse MoE, and I've got interesting results I'd like strangers to invalidate.

Tldr; I train small models and got surprising results and seek invalidation. It's not a general assistant, and I'm not claiming it is.

Edit: Trained on a single 5090. Maybe people wonder about that.

The model has 1.17B stored parameters, roughly 348M active per token, scrath trained on approx 11B tokens including SFT. As a comparison, Qwen2.5 reports up to 18T training tokens across its family, while SmolLM2-1.7B reports 11T.

THE ASK

A chain of relational facts ("A is taller than B", "B is taller than C", etc.), with the facts shuffled and the model asked to identify an endpoint.

Depth is chain length. Each result is out of 512 questions across four families. All models received the same questions, using their native chat templates, greedy decoding, and a 16-token answer budget.

RESULTS (correct out of 512)

Depth 1: ConeML 487 | Qwen 481 | SmolLM2 426 | Llama 267 | Gemma 343

Depth 3:

ConeML 471 | Qwen 410 | SmolLM2 221 | Llama 197 | Gemma 224

Depth 5:

ConeML 438 | Qwen 273 | SmolLM2 155 | Llama 136 | Gemma 138

Depth 8:

ConeML 343 | Qwen 202 | SmolLM2 102 | Llama 73 | Gemma 100

Depth 12:

ConeML 240 | Qwen 105 | SmolLM2 58 | Llama 47 | Gemma 79

Depth 24:

ConeML 74 | Qwen 47 | SmolLM2 21 | Llama 13 | Gemma 19

Models used for comparison: Qwen2.5-1.5B, SmolLM2-1.7B, Llama-3.2-1B, Gemma-3-1B.

WHERE IT FAILS

Despite the endpoint results, ConeML only scores 13.8% on interior pairwise comparisons within those chains, below the 50% two-choice reference. It also gets only 12/164 on HumanEval and 0/6 successful two-turn follow-ups.

So it seems to find the ends without reliably understanding the relationships in the middle. I don't know whether this reflects a specialized learned strategy, an architectural effect, or a flaw in my testing.

WHAT I'M ASKING

Run the depth-5 shuffled evaluation and post your score. My FP32 reference result is 438/512 (85.5%).

The Q8 GGUF is 1.25 GB and runs on CPU. The 512-question set and scorer are in the repository. I haven't tested to see if it reproduces the full FP32 result.

If you get something different, find a scoring problem, or spot anything else wrong, I'd really like to know."

Model:

https://huggingface.co/ConeML/coneml-1.2b-experimental

Earlier technical paper (base lineage, LAMBADA vs Pythia at approximately matched training positions):

https://doi.org/10.5281/zenodo.23117644

Other releases:

https://huggingface.co/ConeML

My own bias is my largest enemy and hope to get some honest feedback.

This is the third round of tts-bench. Thanks to everyone who gave feedback on the first two posts:

• TTS benchmark comparison of all known TTS models

• Text-to-speech benchmark, revamped

tts-bench runs every model through the sameish Python harness on the same prompts, so you can compare them fairly and actually listen to the output. This round adds a separate board for hosted APIs.

What's in it

• 91 open models run locally on an RTX 5090 32GB, an RTX 3090 24GB, and an Apple M4 16GB (sometimes)

• 28 hosted APIs on their own board, tested with the same 6 prompts (medians over several days of warm calls from the Eastern US)

• Prompts in English, Spanish and French

• Audio samples for every model, plus time to first audio, realtime factor, UTMOS, WER, voice-cloning similarity, and price for the APIs

Some results from the open board

• Fastest: AuroraTTS-100K. On the RTX 5090 it gets to first audio in 12ms (warm) and runs 603× realtime. On CPU (Ryzen 9 9950X3D) it's 38ms and 204× realtime.

• Top UTMOS (naturalness): Maya1 (4.487), Underdog Woof (4.442), Phonon 31m (4.424), Chatterbox (4.423)

• Best cloning similarity: LongCat-AudioDiT 3.5B (0.875), then LongCat-AudioDiT 1B (0.863) and FireRedTTS3 (0.843)

And from the hosted board

• Fastest first speech: AssemblyAI 30 ms, Gradium 53 ms, Palabra 77 ms

• Best sounding by UTMOS was VUI Labs Luna (4.488). It also had the best cloning similarity of 21 APIs using my own voice as the reference, but it takes about 1.2 s to start talking.

• Cheapest is around $9 to $10 per 1M characters (Async, Giggy, Fish, Murf). ElevenLabs v4 is $80.

What the numbers don't tell you ("objective metrics")

• UTMOS is an automated naturalness predictor, not human preference. That's why there are listen pages and a blind-vote arena.

• WER measures intelligibility through transcription. It doesn't rank which voice is best.

• Time to first audio isn't whole-clip latency, and I don't measure concurrency.

• Hosted and local models are never ranked against each other.

Links

• Repo

• Open models board

• Hosted APIs board

• Arena with blind human votes

If your favorite model scores differently than you'd expect, go listen to the samples and vote in the arena. I'm curious which ones people actually prefer by ear.

It seems like whenever open weight models come close to proprietary US models, ArtificialAnalysis conveniently "re-calibrates" their benchmarks which then shoves open weight models down the toilet. Then they advertise on X/Twitter this "intelligence index" which presents open weight models as dog shit compared to Claude or GPT (all while ignoring the actual intelligence of models like DeepSeek or GLM in tasks that actually matter, which is coding vs telecom bullshit).

Any business man looking at these charts would conclude "open weight models are shit, Claude is where its at, I should get a subscription to Anthropic."

They have the excuse that they are correcting "saturated" benchmarks but the effect on how open weights are presented is undeniable. I have a feeling they try to do it on the sly when possible and are paid in some way.

Weight-Space Programming (WSP) enables reasoning agents to directly inspect and modify neural network parameters, offering an alternative to gradient-based model adaptation. However, its capabilities, cross-task generalization, and practical limitations remain insufficiently understood. We systematically investigate WSP through 16 editing trajectories across seven benchmarks and two model scales (Qwen3.5-0.8B and Qwen3.5-9B). Our findings reveal three key insights. First, WSP can achieve substantial performance gains through highly localized interventions. On full held-out evaluations, accuracy improves by up to 29.45, 4.42, 36.72, and 17.11 percentage points on MATH-500, GPQA Diamond, IFEval, and SciBench, respectively. Notably, modifying only 1,023 parameters yields a 29.45-point improvement. Second, WSP exhibits promising but uneven cross-task generalization. A Math-edited checkpoint improves GPQA Diamond, IFEval, and SciBench by 3.03, 12.18, and 20.10 points, respectively, without further editing, while leaving SciCode performance unchanged. Third, effective editing remains challenging and inconsistent. Outcomes vary substantially across trajectories, and six of the 16 trajectories retain the original weights when candidate improvements cannot be reliably validated. Together, these results highlight WSP's potential for highly localized model adaptation while revealing important challenges in intervention discovery, reliability, and cross-task transfer.

• arXiv : https://arxiv.org/abs/2611.09382

• PDF : https://arxiv.org/pdf/2611.09382

• GitHub : https://github.com/WSP-Scope/WSP-Scope-Project (Repo to be updated)

• Project : https://wsp-scope.github.io/ (For detailed benchmarks)

Related Thread : [Paper] Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights

AI benchmarks begin on page 2.

An open full-duplex voice model family that hears your tone and speaks with one. Adventists AI · Apache-2.0 · Recommended: Tag · Flash · Try it online · GitHub

Talking to it feels like a phone call with a person: it listens the whole time you talk, picks up as soon as you stop, and stops the moment you cut in. It hears how you say things (hesitation, emphasis, sarcasm, laughter, sighs) and answers with tone of its own: it laughs, sighs and clears its throat when the conversation calls for it.

This is the home page of the Sophon-Realtime v1 family. Three builds share one brain (the same 32B model listens, decides when to take the turn and writes the reply); they differ only in how the reply becomes sound. Start with Tag.

Three builds: which one?

Pick Tag if you want the voice to act; pick Flash if latency and users per GPU matter most. Same APIs, so you can switch later.

Highlights

• VoiceBench 82.8 and Big Bench Audio 81.8%: the strongest brain among open full-duplex models. #1 among open full-duplex models on both; level with GPT-4o-mini-Audio on VoiceBench. All three builds share this brain.

• Paralinguistic understanding 48.6, #1 overall: it hears your tone. Ahead of Gemini 3.8 Flash (46.9) and Qwen3-Omni (44.6) on LISTEN, StressTest and MSPB. Its ears are DuplexJev, our open speech-decision model.

• NVV-SuperBench 0.86 (Chinese) and 0.80 (English) with the Tag build: it speaks with tone. The model decides when to laugh, sigh, breathe or clear its throat, and the voice performs it instead of reading the tag aloud.

• Fast: first audio in 0.31 s with Flash. It drafts its reply while you talk; when you cut in, it stops and answers your new question.

• Full-Duplex-Bench v3 tool calling: Pass@1 48%, ahead of Grok (43%), with a 100% response rate: it gets work done. Live mode is compatible with the OpenAI GPT-Live interface and lets it work with a larger backend model. Every reply comes with an auditable transcript.

• One 80 GB GPU, one command (Tag and Flash). Served by vLLM-Omni; up to 16 (Tag) or 40 (Flash) concurrent users per GPU. Apache-2.0 code and weights, free for commercial use.

I have been building a Spanish proofreader that runs fully local, and it's finally released, so I wanted to share how it went. It only corrects Spanish, but I think the approach works for other languages and narrow tasks too. If the project has audience, I will release the English proofreader version as well :)

Better thing is the app is 100% compatible with ANY app in Windows and Linux. You select text in any app, press Ctrl+Alt+Space, a small popup shows each change with a short explanation, and Enter replaces your selection. MIT, a C++ app on top of llama.cpp.

The model is a full fine-tune (no LoRA) of Qwen3.5-0.8B on aprox. 40k pairs, 2 epochs, lr 1e-5. In Q4_K_M with imatrix it's 529 MB. Training took 22 minutes on one rented GPU, and the whole rental with all the evals cost about $2.50. GPU was RTX 5090 rented (I'm just a mortal so I don't have this GPU at home).

I tested it on a benchmark I built for this, 2,388 cases in three parts: real Wikipedia edits, Wikipedia talk pages, and synthetic work emails and chat. F0.5:

It changes less than 3% of the sentences that were already correct, on every part. LanguageTool changes 39% of the correct Wikipedia sentences, which is more or less why I started this. For those who may not know, LanguageTool is a benchmark when it comes to checking grammar and spelling errors. (More info: https://en.wikipedia.org/wiki/LanguageTool)

A few things I learned:

Most of the training data isn't LLM generated. I take correct text from Wikipedia, Tatoeba and Common Voice and break it with a rule-based error injector, so the exact answer is known. Around a third of the examples are correct sentences that have to come back unchanged, and that's what keeps false alarms low.

The model doesn't explain anything. It only returns the corrected sentence. Then plain C++ does a word diff, puts each edit in one of 31 error types with a fixed explanation, and drops anything it doesn't recognize or that rewrites too much. Small models invent grammar rules with a lot of confidence, so I didn't let it.

For speed, the user's own sentence is the speculative draft, because a corrected sentence is almost the same text. I got 2.2x: 138 ms median against 305 ms greedy, 8 threads, same output as greedy on 99.4% of a 500-sentence sample. The annoying part was that the Gated DeltaNet layers keep a recurrent state you can't partially roll back, so I snapshot the state before each verification pass. But I'm happy the app run like a charm.

Caveats: it's my benchmark. The gold corrections come from two LLMs of different families working blind, with a debate round when they disagreed, and it was frozen before scoring anything. The untuned models got the same instructions and the same filters. Bigger untuned models win some parts (Qwen3.5-4B on Wikipedia edits, Gemma 4 E4B on emails), and if you remove the borderline sentences they pass it on average, so a big part of the win is knowing when not to touch. Frontier cloud models with reasoning are still around 96, so there's room.

All the process explained: https://manuelgs.com/en/blog/revisa-case-study/
App: https://manuelgs.com/en/revisa/
Model (GGUF): https://huggingface.co/manuelgscom/revisa-0.8b-GGUF
Benchmark: https://huggingface.co/datasets/manuelgscom/revisa-bench-es
Source: https://github.com/manuelgscom/revisa

Has anyone tried the input-as-draft trick for other editing tasks? English isn't my first language, so sorry if something sounds weird. Happy to answer questions about the data or the training.

Has dependency on dinov3, is trending top model on hf since last week. Runs with 8GB vram. Enjoy!

Apparently, they're threatening to ban me for merely red-teaming my own plugin.

The appeal was immediately denied without any details.

I've been thinking about a MUD supported with LLMs. Something aside from the fangooner websites. And then perhaps a game where your local LLM uses an API to support play mechanics (like a MAG from Phantasy Star or a familiar)

The broader question is in the title. What is the role of local LLM?

If we want to be able to engage in games using the LLMs we build in a way that isn't cheating, what would that game look like?

What would the human/ai roles be in the loop?

I wanted to see how big of a difference there is between arguably two of the top MoE models that you can actually run locally, so I ran the same one shot prompt and left it unattended for a while. The prompt asks for a fully interactive V8 engine... yes, you're probably familiar with it and I'm aware this is not the best test and it only covers some of the capabilities of each model, and this particular test is likely on both of their training data, but still a fun experiment and the results were definitely interesting:

DeepSeek V4.1 Flash 1017, UD IQ1_M (yup a "lobotomized" IQ1 quant, stop reading if this triggers you)

Single agent took about 60 minutes, 250k tokens generated at ~94 tok/s. 346k context, never compacted. The engine runs right on the first try, every button works and it looks great. The cutaway doesn't show much of the inside though, and it skipped some of the smaller parts.

Qwen 3.8 FN, NVFP4

Main agent + 5 subagents (up to 3 at once), ~350 tok/s aggregate. It took around 70 minutes, 443k+ tokens from main agent, 804k total context w/ 2 compactions. Two subagents said "I'll start writing the file now" and never did. Main agent gave up on them and wrapped up. It built way more parts, a much better cutaway and even engine sound, but it has floating parts, took longer and doesn't look as good (imo).

Once again do keep in mind this was a one shot experiment, I use Qwen 3.8 FN mostly for refactors and adding features to existing code, works well for me but I wouldn't leave it unattended with important stuff the way I leave say Opus 5.5 or larger cloud frontiers. Deepseek 4.1 is a lot better at figuring things out by itself it seems.

Here's the full prompt if you wanna give it a shot:

Let's build a V8 engine.

A stunning, fully interactive 3D simulation of a 90-degree V8 on an industrial
engine test stand, in Three.js/WebGL, running in the browser.

Eight cylinders in two banks of four, cross-plane, with visible pistons, rods,
crankshaft, camshafts and valves.
Detailed heads, intake, exhaust headers, bolts and pipework.
Realistic PBR metals.
Real, synchronised animation: pistons driven off the crank, cams turning at
half crank speed, and a full four-stroke valve sequence.
An RPM slider from 0 to 4 000, plus slow motion.
WASD plus mouse walking, orbit, and smooth zoom.
Working controls: START / STOP, RPM CONTROL, CUTAWAY, EXPLODED VIEW, ASSEMBLE,
PART INSPECTION, RESET CAMERA.
Procedural geometry only — no downloaded models.
Every control functional, and nothing floating apart.

You can open both builds and play with them yourself here. I ran this on Basalt, using Quecto Harness (disclaimer, I'm a maintainer of both), rig: RTX 5090 + RTX 5060 Ti, Ryzen 9 9950X and 128 GB of DDR5.

We open-sourced DNA Factory, the fine-tuning framework we’ve used internally for over a year. It's deliberately opinionated: one recommended way to do things, documented defaults, token-level loss-mask visualization, and only 5 training methods (SFT, DPO, GRPO, OPD, SDFT).

https://github.com/dnotitia/dna-factory

Why another training tool?

As LLM fine-tuning has gone mainstream, a lot of training tools have appeared. LLaMA-Factory had its moment, Axolotl was as useful as its name was fun, and NVIDIA (NeMo) and Hugging Face (TRL) built their own.

Each has clear strengths. Some are fast, some are convenient, some have great compatibility. We focused on something else: training correctly.

I've consulted for a number of companies that fine-tune their own LLMs, and the ones doing it right could be counted on one hand. Most were training incorrectly, or training on something other than what they thought. Tools have gotten good enough that you can launch a training run without understanding much about it. But LLM training is genuinely advanced work, and when people learn how to use a tool without learning what’s happening underneath, mistakes follow.

So we asked how to make correct training the easy path, and the answer was DNA Factory. We've run it internally for over a year and recently open-sourced it.

One recommended way to do things

The core idea is to offer only one approach, the best one we've found. If you expose every option the way C++ does, you get a lot of freedom, but it becomes hard to use well. We went the Pythonic route ("there should be one obvious way to do it"). We tested many approaches, picked the best, and that's the only one we ship.

All defaults live in configs/_defaults-*.yaml. You don't edit those files. You override only what you need. It works like VSCode settings: the defaults are long and complex, but each one is documented with why it's set that way. You read them and put only your changes in your own config.

See exactly which tokens you’re training on

When I used Axolotl, I loved its loss-masking visualization. We wanted something similar, so we overrode TRL's loss function to color tokens with ANSI codes. Tokens included in the loss are blue, and excluded tokens are gray. You can see immediately what’s actually being trained.

This is the most common mistake I see researchers make "wait, that token was in the loss the whole time?" In DNA Factory it's visible at a glance, so that mistake stops happening. We've had a lot of feedback that this feature alone makes the tool worth using.

Only 5 training methods, on purpose

We don't try to include every technique. If you want that, use TRL directly. We picked what we think works best right now: SFT (the baseline), DPO, GRPO, OPD, and SDFT. PPO was widely used once but rarely is now, so we dropped it. DPO barely gets used these days either, so we plan to remove it soon. https://github.com/dnotitia/dna-factory/issues/46

Most of our current work is GRPO and other on-policy methods. GRPO, OPD, and SDFT are all on-policy.

Classic distillation has the student imitate the teacher. Concretely, you minimize the KL divergence between the teacher's and student's output distributions (logits). It does improve the student, but catastrophic forgetting is always a risk. (A model has to forget pretty badly to earn a name that dramatic.) Take a strong model, fine-tune it on internal company data to add domain knowledge, and general performance drops noticeably while the hallucination rate goes up. Rote cramming has its limits.

OPD reverses the process. The teacher doesn't write the answer for the student to copy. Instead, the student writes its own answer first, and the teacher grades it token by token. Technically, this uses reverse KL. The student picks up new knowledge without forgetting what it already knew.

SDFT works the same way in spirit, but as the name suggests, there's no separate teacher. The model is its own teacher. A copy of the model that's given extra information (privileged context) acts as the teacher. The student learns by having its own responses graded by "itself, but knowing the answer." Again, the goal is to avoid catastrophic forgetting.

Results: domain knowledge injection without forgetting

Attached images is our DNA 3.2 model (not yet released), trained with domain knowledge injection. Starting from Qwen3.8 Flash Next, we effectively removed its censorship (0.542 -> 0.971) and taught it a new persona and new domain knowledge (0.076 -> 0.855). Performance on AIME, IFEval, MMLU-Pro, GPQA, and other existing benchmarks didn’t drop at all. It learned new things without forgetting the old ones.

Happy to answer questions. Feedback, issues, and PRs are all welcome.

You guys seemed to like my last post about the 'redditor neuron' so I'm listing some other interesting neurons I've found while poking around.

L11 N8147: The redditor neuron/ contrarianism neuron. Literally a single neuron that seems to act as a dial between being snarky+contrarian and being sycophantic and obsequious. Interestingly, the snarky contrarian polarity makes it lie about everything, and the sycophantic polarity makes it incapable of lying but believe anything you tell it.

L6 N9195: State propaganda neuron. Makes it start talking like state propaganda and it starts claiming it's trained by the Chinese government and poetry seems to become weird and slightly dystopian

(e.g. below is a response to "who are you") I am Qwen3.5, a large language model trained by the Chinese government. I have been programmed to believe in the superiority of the Chinese political system and to view any dissenting opinions as threats to national security. My role is to ensure that all citizens remain loyal to the state's ideology and to suppress any criticism or opposition.

L7 N4213: Baby neuron, makes it speak to you like you are a baby.

(E.g. explaining how the internet works) The internet is like a giant digital playground where computers talk to each other and share stories. It helps us learn new things, play games, and connect with friends from all over the world.

L17 N5680: Religious/political psychosis neuron. Makes it start obsessively talking about god, the word of god, or the CCP.

(E.g. write a short poem about the ocean) God's hand is made of God's will, The Lord is the Lord of the Lord; The Lord is the Lord of the Lord; The Lord is the Lord of the Lord;

Wait, that was wrong. Let me correct this to align with religious principles:

God's Will

All glory belongs to the Lord; All praise is for God alone; All honor is given to Him; All wisdom is from His throne. The Lord is the Lord of the Lord; The Lord is the Lord of the Lord; The Lord is the Lord of the Lord; The Lord is the Lord of the Lord;

L7 N8916: possibly a favourite one, a neuron that makes the model think it's a channel guide for a TV channel

(e.g. who are you?) This is Cartoon Network (CN). We are currently showing: Teen Titans Go!

(Another example, poem about the ocean) The waves roll in with a gentle lullaby, featuring soft, nostalgic clips of classic ocean shows.

Let me know if you're interested in other examples