/AI2h ago

LLM systems researcher Yupo Niu argues RL environment data overhangs enable transfer evaluation, while Miles Brundage defends true generalization

Brundage notes self-correction generalizes without task-specific reinforcement learning

21200494

#20

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1a3orn@1a3orn#1393inAI

That is, having 10k such environments sets you up well to check what algos give you the best transfer from one 5k to the other 5k.

This is a reason that the fake generality of RLVR-trained LLMs doesn't rule out short timelines entirely; there's a RL env data usefulness overhang.

1a3orn@1a3orn

RLVR-trained LLMs probably don't generalize "broadly" -- their broad intelligence comes from being trained on a huge diversity of RL envs.

However, Ant / OAI owning a huge diversity of RL envs will make it easier for them to study what algos *do* generalize broadly.

4:37 PM · Jun 8, 2026 · 298 Views

/AI2h ago

LLM systems researcher Yupo Niu argues RL environment data overhangs enable transfer evaluation, while Miles Brundage defends true generalization

Brundage notes self-correction generalizes without task-specific reinforcement learning

21200494

#20

Original post

1a3orn@1a3orn#1393inAI

That is, having 10k such environments sets you up well to check what algos give you the best transfer from one 5k to the other 5k.

This is a reason that the fake generality of RLVR-trained LLMs doesn't rule out short timelines entirely; there's a RL env data usefulness overhang.

1a3orn@1a3orn

RLVR-trained LLMs probably don't generalize "broadly" -- their broad intelligence comes from being trained on a huge diversity of RL envs.

However, Ant / OAI owning a huge diversity of RL envs will make it easier for them to study what algos *do* generalize broadly.

4:37 PM · Jun 8, 2026 · 298 Views

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Miles Brundage@Miles_Brundage

@1a3orn I think there's some of both going on. Things like self-correction, task decomposition, brainstorming etc. are generalizable skills that do apply to many tasks + don't require RL to be done on those specific tasks. But, they can certainly be refined much better per-task

1a3orn@1a3orn

That is, having 10k such environments sets you up well to check what algos give you the best transfer from one 5k to the other 5k.

This is a reason that the fake generality of RLVR-trained LLMs doesn't rule out short timelines entirely; there's a RL env data usefulness overhang.

2h12730

1a3orn@1a3orn

@Miles_Brundage Yeah I agree tbc; you can use RLVR to get (pretty weak) "out of domain" generalization even on very small models.

It's just that you can probably grow this cross-task several OOMs (or, OOMs of a hypothetical good measure for cross task transfer...)

https://arxiv.org/pdf/2509.25123

Miles Brundage@Miles_Brundage

2h6920