/AI7h ago

Training AI Models to Verify Errors Doubles Math Accuracy, Boosts Reasoning 14x

93222821619.9K
#1112Aditi Raghunathan
Original post
Aditi Raghunathan@AdtRaghunathan#1112inAI

We've always intuited that verification is easier than generation. Chen's new work shows that explicitly training for it unlocks massive self-improvement:

馃搱 14脳 boost in test-time refinement on hard reasoning 馃殌 30% gain beyond the RL plateau at training time

Chen Wu@ChenHenryWu

Self-improvement depends on whether a model can judge its own work. We usually train models to generate better - why not train them to verify just as well?

We show how to train models to pinpoint their errors, and the same model nearly doubles its accuracy on hard math and jumps 14x on scientific reasoning. 馃У1/5

8:50 AM 路 Jun 8, 2026 路 9.3K Views
Sentiment

Users highlight the self-trained verifier as equally important to the generator because it doubles hard math accuracy and boosts science reasoning in AI models.

Pos
100.0%
Neg
0.0%
1 comments with sentiment.
Cluster Engagement
Posts from X
Most Activity
Most Activity
VIEWS1.5KBOOKMARKS3LIKES6
alphaXiv@askalphaxiv

read more: https://www.alphaxiv.org/abs/2605.30290

1dViews 1.5KLikes 6Bookmarks 3
RETWEETS23
alphaXiv@askalphaxiv

"Self-Trained Verification for Training- and Test-Time Self-Improvement"

Reasoning models improve faster with a good verifier, but verifiers can't learn to catch subtle errors on their own.

However, a model that can't spot its own mistakes usually can when shown the correct answer.

This paper trains the verifier to imitate that "answer-in-hand" version of itself, then runs it without the answer at test time.

It roughly doubles accuracy on hard math and lifts science reasoning 14x. Training the generator against this verifier also pushes past where standard RL stalls.

1dViews 10.6KLikes 232Bookmarks 147
Guilherme O'Tina@guilhermeotina

the reference-asymmetry trick is clever for verifier training. but i wonder how this plays out beyond math where theres one right answer. for code gen or open-ended tasks with valid alternative solutions, does 'pretend you have the answer' still work or does it penalize valid approaches?

1dViews 220Bookmarks 1
David Geoghegan@Dave_Geoghegan_

@askalphaxiv matches what i see with agents, they catch errors fine in someone elses output and sail straight past their own

1dViews 93
Kamesh 馃嚭馃嚫@ElangovanKamesh

@askalphaxiv The verifier is starting to look just as important as the generator.

16hViews 22