SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization · HackerPapers discussions

SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization — mentioned by homarp

Fri, 09 Oct 2026 19:22:33 GMT

https://arxiv.org/abs/2503.23303 and https://arxiv.org/abs/2510.01237

Paper and discussion history. A mention is not an endorsement.

SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization — mentioned by fxwin

Mon, 21 Sep 2026 09:17:21 GMT

> not to mention the egregious target leakage I was curious about this so I skimmed the paper [0]: > SalesRLAgent achieved 96.7% accuracy, outperforming the best commercial alternative by 23.7 percentage points and the best LLM approach by 34.7 percentage points. For a fuzzy natural language task like this, this magnitude of improvement should already set off alarm bells (Though i admit I'm not even sure what accuracy is even measured here, and the paper doesn't help either). Also, "best LLM" here refers to GPT-4 (at the time of upload, the public already had access to GPT-o3 and). I would…

Paper and discussion history. A mention is not an endorsement.

SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization — mentioned by tomsyouruncle

Sat, 19 Sep 2026 15:00:03 GMT

I’m not filled with confidence when the author’s first paper takes an RL approach but then doesn’t use it to change the action taken in the next turn. Seems like simple classification would achieve the same end. And this quote from the paper isn’t overly reassuring: “I personally found that this sequential approach captured sales dynamics much more effectively than traditional classification models.” https://arxiv.org/pdf/2503.23303

Paper and discussion history. A mention is not an endorsement.

SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization — mentioned by verdverm

Sat, 19 Sep 2026 14:54:05 GMT

paper the reddit OP "published" (their words on reddit) to arxiv (before they put the vouching process in place). It's what you expect if you click through. https://arxiv.org/pdf/2503.23303 Does not appear to be like what Jev is doing, they talk about RAG and embeddings and orchestrators (the stuff that was cool 1 year ago), no talk of system 1 vs 2 (before Jev), whereas Jev is apparently just a model. There is a vLLM PR introducing Jev like capabilities for diffusion models (and more, have not delved deeply) https://github.com/vllm-project/vllm/pull/57250

Paper and discussion history. A mention is not an endorsement.

SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization — mentioned by klibertp

Sat, 19 Sep 2026 12:08:45 GMT

Jev was built using the same architecture Laya's author proposed[1] in March 2025. Laya is an open-source system based on that research from a year ago. Whether Jev is also based on the OP's materials or independently invented is hard to say. [1] https://arxiv.org/abs/2503.23303

Paper and discussion history. A mention is not an endorsement.

SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization — mentioned by verdverm

Sat, 19 Sep 2026 07:28:12 GMT

paper the reddit OP "published" to arxiv (before they put the vouching process in place, it's what you'll expect if you click through) https://arxiv.org/pdf/2503.23303 Does not appear to be like what Jev is doing

Paper and discussion history. A mention is not an endorsement.

SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization — mentioned by corysama

Fri, 18 Sep 2026 16:28:36 GMT

You might also be interested in "Open-sourced jev architecture last year with model,paper and dataset" https://news.ycombinator.com/item?id=49736660 https://www.reddit.com/r/LocalLLaMA/comments/1wjieap/made_th... Papers: https://arxiv.org/abs/2503.23303 https://arxiv.org/abs/2510.01237 Model: https://huggingface.co/DeepMostInnovations/sales-conversion-... Dataset: https://huggingface.co/datasets/DeepMostInnovations/saas-sal...

Paper and discussion history. A mention is not an endorsement.

SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization — HN submission

Thu, 17 Sep 2026 05:13:00 GMT

Open-sourced jev architecture last year with model,paper and dataset

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. For anyones information the main guiding model is RL not embedding model or LLM Paper: https://arxiv.org/abs/2503.23303 Model…

97 points; 18 discussion replies on HN. Paper and source history. A submission is not an endorsement.

SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization — HN submission

Tue, 13 May 2025 02:39:16 GMT

Open-Source RL Model for Predicting Sales Conversion from Conversations

For the past couple of months, I have been working on building a chess game kinda system for predicting sales conversion probabilities from sales conversations. Sales are notoriously difficult to analyse with current LLMs or SLMs, even ChatGPT, Claude, or Gemini failed to fully analyse sales conversations. How about we can guide the conversations based on predicting the conversion probabilities, that is, kinda trained on a 100000+ sales conversation with RL to predict the final probability from the embeddings. So I just used Azure OpenAI embedding(especially the text-embedding-3-large model…

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.