Skip to main content
← Back to news

ProRubric: fighting reward hacking in rubric-based RL at the protocol level

Research
Source:Hugging Face
In rubric-based reinforcement learning, additive aggregation of criteria lets a policy compensate for one decisive error with advice nobody asked for. ProRubric (ByteDance) groups criteria into protocol-level dimensions that count only when all their criteria hold and no failure clause fires: on clinical consultations, appropriateness rises 10.8 points without losing coverage, with the criteria unchanged word for word.
Read original article

Related articles

Where local models learn: open weights explained without the fluff

The model doesn't hook into the internet for its abilities: it absorbed them once at pre-training and executes them on your hardware. Open weights isn't open data, and the models come from many labs, not one.

A Model Without a GPU: When the CPU Is Actually Enough

In 2026, CPU-only inference is no longer a fallback: when it genuinely works, what speed to expect, and which levers to pull to squeeze the most out of your processor.

Decision models in local: JEV-35B and the 3B-active System 1

You don't need a big LLM to decide. JEV-35B (35B, 3B active) in NVFP4 on a 32GB GPU and ej (11.4MB) on CPU show how to do routing, classification and quality control without generating a single token.

Get the next local-AI briefing

Once a week, the top selection from iaLocale: articles, news and resources on local AI, and more.

After signing up you receive a confirmation email (double opt-in). Without confirmation we send nothing. No spam, we promise.