Rosmine

@rosmineb.bsky.social

Senior ML Scientist @ FAANG working on LLMs DM me ml questions

Overview of GRPO (Group Relative Policy Optimization) GRPO is an improvement on PPO introduced in the DeepSeekMath paper The motivation is that PPO requires 4 large models, a policy, value function, reward model, and reference model. GRPO removes the need for the value model.

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I want to train a transformer model to be a random number generator At first it sounds dumb, but you could leverage GPU non-determinism to make it truly random, not just pseudo random There are better ways to do rng so I still think it's a bad idea, but a cool bad idea

A Critical Evaluation of AI Feedback for Aligning Large Language Models Investigated paradigm of modifying model behavior by first doing SFT training using data from teacher model, then following with RLAIF training by teacher reward model They found:

Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding This paper introduces LaTent Reasoning Optimization (LaTRO), a training framework - Improves zero shot accuracy by +12.5% on GSM8K over base models. - Doesn't use external feedback or reward models ...

Trying a new ML research area is tough. Every time I think I have a good new idea, I google and find there are already 50,000 papers covering it. I cannot read 50,000 papers per day

Training Verifiers to Solve Math Word Problems (2021) - This paper introduced GSM8K, and showed how using verifiers can significantly improve performance (up to 20+ percentage points compared to finetuning, see graph below)

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I made a script to help you quickly become an expert in an ML subfield How works: 1. Input a few papers 2. Input a description of the subfield. Could be generic like "optimizers" or highly specific like "improvements on LoRA" 🧵

Is it possible to tell if a model was trained on test? I thought it would be easy to tell if a model was trained on test by using Membership Inference Attacks I looked into it more and turns out it's a lot harder than I thought because...