RewardHarness: Self-Evolving Agentic Post-Training
Paper β’ 2605.08703 β’ Published β’ 10
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DongfuJiangΒ
authored 3
papers 6 days ago
alielfilali01Β
posted an update 13 days ago
Post
404
Plans in HTML > Plans in Markdown
Post
1012
Sharing how I built the LongCat-Video-Avatar 1.5 Space (+500k views on X) in one agent session. Gave a coding agent its own AI lab on ZeroGPU, framed the goal, walked away. It designed, deployed, tested against the live API, fixed, shipped.
Full recipe with the copy-paste prompt: https://huggingface.co/blog/victor/building-zerogpu-spaces-autonomously
Full recipe with the copy-paste prompt: https://huggingface.co/blog/victor/building-zerogpu-spaces-autonomously
- 1 reply
Post
132
One prompt, three answers - which model is from where?
johko/llm-blind-date
I built a little demo where you give three models (Apertus, Llama, Qwen3) the same prompt and in the end you have to guess which is which just based on their answers.
GIve it a try! ;)
johko/llm-blind-date
I built a little demo where you give three models (Apertus, Llama, Qwen3) the same prompt and in the end you have to guess which is which just based on their answers.
GIve it a try! ;)
Post
3552
I built HF Radio on Hugging Face Spaces π»
fffiloni/HF-Radio
A live community radio for AI-generated songs, powered by tracks created with ACE-Step.
You can tune in, discover community-made songs in many languages, vote on what sounds good, and mark your real favorites as Bangers.
The more people listen, vote, and create, the better the station gets.
Under the hood, it connects a few Hugging Face pieces together:
Spaces for the live app, HF buckets for community tracks, OAuth for signed-in listeners, server-side streaming with ffmpeg, hourly playlist refreshes, moderation, jingles, and community feedback loops.
Itβs not just a playlist.
Itβs a shared taste experiment:
new songs get a shot every hour, and the community helps decide what deserves another spin.
Come listen.
Find weird gems.
Support the Bangers.
Shape the radio.
β> fffiloni/HF-Radio
fffiloni/HF-Radio
A live community radio for AI-generated songs, powered by tracks created with ACE-Step.
You can tune in, discover community-made songs in many languages, vote on what sounds good, and mark your real favorites as Bangers.
The more people listen, vote, and create, the better the station gets.
Under the hood, it connects a few Hugging Face pieces together:
Spaces for the live app, HF buckets for community tracks, OAuth for signed-in listeners, server-side streaming with ffmpeg, hourly playlist refreshes, moderation, jingles, and community feedback loops.
Itβs not just a playlist.
Itβs a shared taste experiment:
new songs get a shot every hour, and the community helps decide what deserves another spin.
Come listen.
Find weird gems.
Support the Bangers.
Shape the radio.
β> fffiloni/HF-Radio
Post
2849
ππ»ββοΈ Hey there folks ,
Turns out : if we predict π earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see.
Sentinel-2 imagery π°οΈbasically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms.
meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize π‘earth-bound response .
I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time.
At least that's the concept !
check out the blog : https://huggingface.co/blog/Tonic/save-patagonia-by-predicting-earth
- Collection: https://huggingface.co/collections/NuTonic/earth-observation-with-temporal-and-general-understanding
- Code: https://github.com/Josephrp/Nutonic
- Dataset: NuTonic/sat-vl-sft-training-ready-v1
- Model: NuTonic/lspace
- Training: NuTonic/lspace-trackio
- Evals: NuTonic/Patagonia_Eval
Turns out : if we predict π earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see.
Sentinel-2 imagery π°οΈbasically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms.
meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize π‘earth-bound response .
I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time.
At least that's the concept !
check out the blog : https://huggingface.co/blog/Tonic/save-patagonia-by-predicting-earth
- Collection: https://huggingface.co/collections/NuTonic/earth-observation-with-temporal-and-general-understanding
- Code: https://github.com/Josephrp/Nutonic
- Dataset: NuTonic/sat-vl-sft-training-ready-v1
- Model: NuTonic/lspace
- Training: NuTonic/lspace-trackio
- Evals: NuTonic/Patagonia_Eval
- 2 replies
DongfuJiangΒ
authored 4
papers 28 days ago
Watch Before You Answer: Learning from Visually Grounded Post-Training
Paper β’ 2604.05117 β’ Published β’ 36
ClawBench: Can AI Agents Complete Everyday Online Tasks?
Paper β’ 2604.08523 β’ Published β’ 263
Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
Paper β’ 2604.12374 β’ Published β’ 37
Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction
Paper β’ 2605.05242 β’ Published β’ 123
Post
494
Great technical guide by Nico Martin on the Hugging Face blog, showing how to use Transformers.js inside a Chrome extension and run ONNX models from the Hub locally with WebGPU inside a Manifest V3 extension.
The interesting part: this is not just a chatbot in a side panel.
The article walks through the architecture behind a browser agent that can read open tabs, query webpages, search history, and highlight elements directly on the page β with models downloaded from the Hugging Face Hub, cached under the extension origin, and executed locally instead of being called through a remote API for every prompt.
A strong blueprint for building local-first web copilots, reading assistants, and AI-powered browsing workflows.
Article: https://huggingface.co/blog/transformersjs-chrome-extension
The interesting part: this is not just a chatbot in a side panel.
The article walks through the architecture behind a browser agent that can read open tabs, query webpages, search history, and highlight elements directly on the page β with models downloaded from the Hugging Face Hub, cached under the extension origin, and executed locally instead of being called through a remote API for every prompt.
A strong blueprint for building local-first web copilots, reading assistants, and AI-powered browsing workflows.
Article: https://huggingface.co/blog/transformersjs-chrome-extension
Post
337
Iβve been reading βWhat if AI systems werenβt chatbots?β
What if AI systems weren't chatbots? (2605.07896) π
The paper asks a simple but important question: what if the chatbot interface is not just a neutral wrapper around AI models, but part of the problem?
A chatbot can make a system feel more capable, more certain, and more βhumanβ than it really is. That matters, because interfaces shape how we trust, use, and delegate to AI systems.
When everything becomes: ask β answer
we can lose sight of the actual workflow:
- parameters
- alternatives
- uncertainty
- intermediate steps
- failure modes
- human control
For creative AI especially β image, video, editing, animation β Iβm not sure βchatβ should always be the default interface.
Sometimes we need a conversation.
But often we need a canvas, a timeline, sliders, masks, previews, comparisons, and visible pipelines.
This is also why I find many open ML demos interesting: Spaces, Gradio apps, visual tools, small focused interfaces.
They often explore another direction β not just better assistants, but better tools. π€
What if AI systems weren't chatbots? (2605.07896) π
The paper asks a simple but important question: what if the chatbot interface is not just a neutral wrapper around AI models, but part of the problem?
A chatbot can make a system feel more capable, more certain, and more βhumanβ than it really is. That matters, because interfaces shape how we trust, use, and delegate to AI systems.
When everything becomes: ask β answer
we can lose sight of the actual workflow:
- parameters
- alternatives
- uncertainty
- intermediate steps
- failure modes
- human control
For creative AI especially β image, video, editing, animation β Iβm not sure βchatβ should always be the default interface.
Sometimes we need a conversation.
But often we need a canvas, a timeline, sliders, masks, previews, comparisons, and visible pipelines.
This is also why I find many open ML demos interesting: Spaces, Gradio apps, visual tools, small focused interfaces.
They often explore another direction β not just better assistants, but better tools. π€
- 2 replies
PrabhjotschughΒ
authored a
paper about 1 month ago
Post
4309
ππ»ββοΈ Hey there folks,
since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !
Check this one out :
NuTonic/sat-bbox-metadata-sft-v1
the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .
hope you like it ! π
since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !
Check this one out :
NuTonic/sat-bbox-metadata-sft-v1
the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .
hope you like it ! π
- 2 replies
Post
691
Quietly baking Image β Music π΅ v3 β now running on SOTA open-source models.
π fffiloni/image-2-music-v3 | Feel free to test it and share feedback.
Just wiring together: merve/moondream3 * victor/ace-step-jam
Image β prompt β audio | Early version, will evolve | Follow: @fffiloni
π fffiloni/image-2-music-v3 | Feel free to test it and share feedback.
Just wiring together: merve/moondream3 * victor/ace-step-jam
Image β prompt β audio | Early version, will evolve | Follow: @fffiloni
Post
3654
ππ»ββοΈ Hey there folks ,
I'm sharing huggingface's largest dataset of annotated statelite images today.
check it out here : NuTonic/sat-image-boundingbox-sft-full
I hope you like it , the idea is to be able to use this with small vision models π
I'm sharing huggingface's largest dataset of annotated statelite images today.
check it out here : NuTonic/sat-image-boundingbox-sft-full
I hope you like it , the idea is to be able to use this with small vision models π
Post
1823
π RB-Modulation is back on Hugging Face Spaces!
This is an older project that recently broke due to dependency changes, but itβs now fixed and running again β
π Whatβs fixed:
- GroundingDINO & LangSAM installation
- compatibility with recent environments
- GPU inference running smoothly again
π Try it here:
fffiloni/RB-Modulation
Feel free to give it a try again β feedback welcome!
This is an older project that recently broke due to dependency changes, but itβs now fixed and running again β
π Whatβs fixed:
- GroundingDINO & LangSAM installation
- compatibility with recent environments
- GPU inference running smoothly again
π Try it here:
fffiloni/RB-Modulation
Feel free to give it a try again β feedback welcome!
Post
6132
Want to share my enthusiasm for zai-org/GLM-5.1 here too π₯
I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:
- One-shot car physics with real drift mechanics (this is hard)
- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen
- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters
- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!
- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed
You are going to hear about this model a lot in the next months - open source let's go - and thanks z-aiππ
I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:
- One-shot car physics with real drift mechanics (this is hard)
- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen
- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters
- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!
- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed
You are going to hear about this model a lot in the next months - open source let's go - and thanks z-aiππ
- 5 replies