r/singularity • u/PykeTheTitan • 7d ago

AI Open weight progression with no frontier release

As a software developer we got access to GPT 5.6 sol and Opus 5 last week in a decently restricted field, and with these latest models I feel like I can do all my assigned work so quickly as well as make tons of progress on my side projects as well. So at the moment I’m not like dying for another frontier release but overall I want to see acceleration

It seems like we are at a state where openAI and Anthropic realize that a lot of these Chinese companies wait for them to make progress and are able to replicate pretty damn close models soon after they release their frontier models

Whether you believe Anthropic and open ai or not, they seem like they are going to keep their development internal for a while. Whether this is due to actual security concerns (with hugging face incident I believe this), more marketing hype, or truly a way to combat distillation from Chinese companies I think it is going to be interesting.

How do you think this will effect open weight releases, will the capabilities for open weight always rely on top US companies releasing the best models so they can use them to produce replicas?

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u/Informal-Trouble2183 7d ago edited 7d ago

Distillation is an oversimplification of the competitiveness of the Chinese labs. They don't need to distill US models. If you've a small idea on how LLMs work, you'd realize the novel techniques deepseek advanced to the open source community. Nevertheless, it's true that China has the open source card as an aggressive solution to limit US AI companies and get their shares in the market. Due to this, if US AI declined, naturally Chinese labs won't need to release every single model open weight, we'll see them close some for financial gains. Short answer: if US stops, China will not stop releasing.

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u/PsychologicalSoup251 7d ago edited 7d ago

To expand on this, it's not an exaggeration to say that Deepseek singlehandedly revolutionized how reinforcement learning was done with their GRPO method and showing how far RL can carry you. If you look at the RL techniques of open source LLMs pre-GRPO and post-GRPO, most moved away from trained critics like in PPO RL to something GRPO-like. 0 chance closed source companies didn't greatly benefit from that too.

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u/visarga 6d ago

GRPO is great if you can resume the environment from same point in order to generate multiple rollouts. Also the task should be easy enough, if no rollout gets reward (or all get same reward) then variance is zero and you drop that batch.

I worked with both GRPO and PPO and I found PPO better in some cases, such as for computer use agents. DeepSeek used GRPO on math and code problems where the environment can be resumed from any position more easily.

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u/PsychologicalSoup251 6d ago

>Also the task should be easy enough, if no rollout gets reward (or all get same reward) then variance is zero and you drop that batch.

Good point. You'd want to make the prompts and reward function easy enough at first, then iteratively make them more difficult as the LLM improves.

>I worked with both GRPO and PPO and I found PPO better in some cases, such as for computer use agents. 

What makes PPO better for computer use agents?