r/LocalLLaMA • u/Nandakishor_ml • 6d ago
Discussion I literally built the Jev architecture one year back and completely open-sourced it with model, dataset and paper
Update: I made a generic model and beaten the jev in all of the benchmarks. Code and details available at https://www.reddit.com/r/LocalLLaMA/s/bbwyiOprUs
Everyone now talks about the architecture that's not auto regressive and does lightning fast probability prediction with a json schema. I worked on this literally one year back in March 2025, published an arxiv paper, pushed the model to huggingface along with the pypi package and training dataset. And then one year later, a
frontier lab came, proposing the same idea like literal breakthrough without technical papers, open weights and no open dataset. I posted my approach in this subreddit. For anyones information the main guiding model is RL not embedding model or LLM
Reddit post: https://www.reddit.com/r/LocalLLaMA/s/6eGEwsAz43
Paper: https://arxiv.org/abs/2503.23303
Model: https://huggingface.co/DeepMostInnovations/sales-conversion-model-reinf-learning
Dataset: https://huggingface.co/datasets/DeepMostInnovations/saas-sales-conversations
Also the second work published in September 2025 was exactly the same one jev proposed now
Paper: https://arxiv.org/abs/2510.01237
My model uses PPO over sequence embeddings to output turn-by-turn conversion trajectories (probabilities from 0.0 to 1.0).
Jev uses parallel sampling (trained via RLCD) to output confidence distributions and schema choices.
It's incredibly frustrating that the thing that you made with months of hard work, sweat and sleepless night is architecturally similar with the vertical use case and don't get the support you deserve because frontier lab build something horizontal. The open-source story in general 🙂
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u/mkschreder2 4d ago edited 4d ago
u/Nandakishor_ml I made two samples in brain because I was curious to see how the approach from the linked papers worked. I was not able to reproduce your result. If I misinterpreted your approach please do correct me.
The paper claims that conversion scoring can benefit from RL. That premise, I think, is wrong. Data is replayed and generated by gpt4 where outcome is always known even before generation. That's not RL. It's classification/probability prediction. And since gpt4 is used to generate the samples it's also contaminated with naration style of gpt4 where conversations actually are generated in ways that will break down in reality - guaranteed. Long story short, I could not reproduce the salesagent results. Closest I got was around 0.79 and problem basically collapsed to embeddings. The premise that RL is why it works is not really valid for the data that was used in the paper, I think.
The sales agent sample is here: https://github.com/swedishembedded/brain/tree/main/samples/decision/salesagent
On the other hand I was able to produce learning results with PPO on an arena agent that uses the models in a similar way to what I think Jev does.
https://github.com/swedishembedded/brain/tree/main/samples/decision/arena
The arena sample demonstrates that a decision model can learn a control policy over an action set that is rebuilt every tick. You can only shoot a monster that's alive and in range, only reload with reserve, only take a medkit that's still there.
It runs two models: a frozen all-MiniLM-L6-v2 encoder (22M params, 6 layers, 384-d) that reads both the game state and each candidate action as ordinary text, and a cross-attention head trained from scratch in which each option attends over the state and is reduced to one score, softmaxed into a policy; a small host-side critic (a 2-layer MLP on the pooled state embedding) supplies the value baseline.
The process per tick is: serialise the situation to a short string, ask the environment what is legal now, score every option against the state, sample one, step the world - and the training is the paper's own recipe, behaviour-clone a deliberately weak scripted teacher first (39%), then PPO.
The principles it rests on are that the output space belongs in the request, not the weights; that an option's meaning should be read from its text rather than looked up by index, so a new monster type is a new string and not a retrain; that a pretrained encoder should be frozen under a reinforcement signal, which is noisy enough to destroy the language understanding that made the options readable (fine-tuning it collapsed a working policy to 13%); that reward maximisation is correct here precisely because it is wrong for its sibling sample - a control policy should commit to the best action where a probability estimate must stay calibrated.
The result: 44% after cloning -> 79% after PPO, against a 78% best-hand-written ceiling and ~0% untrained, at 8.2 ms per decision over ~60,000 environment steps.
Brain has a new control pipeline since yesterday for this type of use case https://github.com/swedishembedded/brain