Google DeepMind hired professional writers during Gemini 4 Argon's development to eliminate low-quality machine-generated phrasing and improve prose structure

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Reducto trained its R1 model using reinforcement learning loops around mid-training and post-training on edge cases

Dan Schipper identified Gemini 4 Argon as an effective editorial writing partner for structuring complex essays, sharpening metaphors, and performing textual analysis

Prompts are a key unit of post-training for AI models

Historically, AI model post-training was heavily reliant on supervised fine-tuning using the outputs of a very strong model

AI model post-training has evolved from supervised fine-tuning to being heavily reliant on Reinforcement Learning (RL) in complex environments

The iterative process of collecting data, deploying, and refining a model is a robotics version of Reinforcement Learning from Human Feedback (RLHF)

Supervised fine-tuning and Reinforcement Learning from Human Feedback (RLHF) can reduce the output diversity of language models

Reinforcement learning (RL) training can cause models to develop non-human-readable dialects in their chain of thought

RLHF further sharpens a model's output distribution to align with chatbot-like behavior

When RL training only verifies the final outcome, there is no incentive for a model to keep its intermediate reasoning human-readable

The primary difference between many proprietary and open-source models is the post-training process

Instruction tuning is a fine-tuning process that adapts models to the distribution of a user giving instructions to a chatbot

RL training can cause a significant distribution shift away from the human text on which models were pre-trained

Improving the RLHF process could potentially teach AI models to stop hallucinating

ChatGPT is a language model that undergoes an additional training process using reinforcement learning from human feedback (RLHF)

Reinforcement Learning from Human Feedback (RLHF) trains a model by correcting its outputs, teaching it what not to produce

OpenAI hires human trainers to teach models like ChatGPT how to behave correctly

Reflection AI used over 10,000 GB300 GPUs for four weeks to train the Beam model with reinforcement learning

More compute FLOPs were spent on reinforcement learning than on pre-training for Reflection AI's Beam model

The reinforcement learning system for Reflection AI's Beam model showed continuous improvement limited only by available compute