Rahil Verma

Building Applied Compute

[Rahil Verma @RahilVerma13]

We're constantly pushing the frontier here at Applied Compute :)

[Rhythm Garg @rhythmrg]

You can now train your own decision model in our platform AC2! Jev-like models, custom fit for your use case :)

We implement decision models through a language-model backbone with a small learned output layer called a decision head. The backbone processes the input, question, and answer choices, and the head produces scores which are softmaxed to produce a probability distribution over answer choices.

Our software interface makes it easy to add a variable-sized decision head to frontier open-weight models. Choose any open source model, and train it as a decision model using Brier loss or cross-entropy loss. Training updates both the decision head and the language backbone.

We tried it on 50k sampled training datapoints from the Civil Comments dataset, starting from Perplexity’s pplx-decider-v1-27b, and F1 for detecting toxic comments increased from 0.482 to 0.677 on 20k sampled eval datapoints. The training job took less than an hour on four B300 GPUs.

Reach out to get started with your own classification task: https://t.co/D6ArdYexnb

[Rahil Verma @RahilVerma13]

This has saved me countless hours

[Applied Compute @appliedcompute]

We're sharing a key step towards enabling Continual Learning at Applied Compute.

In On-Policy Self-Distillation, the teacher model supervises training with privileged information: a hint. The training signal depends heavily on hint quality, but manually refining hints across runs is impractical, and isolating their individual impact is difficult. We instead optimize hints automatically using efficient proxies for their eventual training value.

[Rahil Verma @RahilVerma13]

@adiaddxyz Our training platform exposes a DecisionClient that parallels a CompletionClient, the main difference being the return type is some form of structured decisions rather than free text.

This drops in well for many existing workflows, worth thinking about what you can optimize!

[Rahil Verma @RahilVerma13]

At Applied Compute most of our RL and eval pipelines grade with an LLM judge. These produce one result per rubric criterion, typically a paragraph of text accompanying a score. For some complicated tasks, grading can be a slow, pricey part of the loop.

We swapped a grader on a code QA benchmark in AC2 for a decision grader: one call, a yes/no probability for every criterion at once. On 100 sample tasks (407 criteria), same answers:
→ ~32× cheaper
→ ~8× faster
→ 94% agreement with the LLM judge

And it’s even better than the original judge:

When the decision grader was confident, it matched the LLM judge 99% of the time. When it wasn't, the decision grader indicated low confidence (30-70%), pointing to ambiguous rubric items. This allowed us to identify noisy tasks and make the dataset cleaner.

A cheap, fast, calibrated grader means you can grade more rollouts, ask more questions per call, and downweight or review the shaky calls instead of training on them.

[Rahil Verma @RahilVerma13]

@ronenbetser We had a pre existing calibrated LLM judge that was asked about 4 criteria in 4 requests - those 4 questions were simply provided as decisions for jev to make!

[Rahil Verma @RahilVerma13]

@ChengGo1199498 Human review found that the rubric was fundamentally flawed for those tasks, hence could contribute to noisy training signal!

[Rahil Verma @RahilVerma13]

There’s nothing cooler than building on the frontier. For the last six months, I’ve been part of the team building AC2 to democratize what we’ve learned while post-training models for the world's most advanced companies. Owning your intelligence has never been easier.

[Applied Compute @appliedcompute]

Today, we're introducing AC2, the Applied Compute Agent Cloud, to enable every team to train, serve, and improve their own frontier models.