Slides: Training Agentic Reasoners — Will Brown, Prime Intellect

Source Video

Training Agentic Reasoners — Will Brown, Prime Intellect

Relationship To World's Fair 2026

These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

Related Scheduled Sessions

Extracted Slides

slide-001.jpg

OCR text:

aWS

eee)

@®Graphite WW Windsurf 4 MoneobB

Mdaily £3 augment code WorkOS

slide-002.jpg

OCR text:

training agentic reasoners

will brown

@willecbb

research lead @ prime intellect

pr world’s fair 2025

‘i a

, | ; a Microsoft ary?

slide-003.jpg

OCR text:

the big labs are all doing it

Continuing to scale reinforcement

learning AND - #

v7) |

Throughout the development of OpenAl 03, we've observed that

large-scale rewnforcement learning exhityts the same “more oF @) | = Ss

compute © better performance™ trend observed in GPT-series

pretrainng. By retracing the scaling path—ths time in RL-we've N = xX a ‘ ¥ x

pushed an additional order of magnitude in both training 9:24:02

compute and inference-time reasoning, yet still see clear ¢" ‘nn

performance gams. validating that the models’ performance

continues to improve the more they're allowed to think. At equal Is RL +LLMs enough for AGI? - :

latency and cost with OpenAl o1, 03 delrvers higher performance .

in ChatGPT— and we've validated that if we fet it think longer, its Sholto Douglas & Trenton Bricken

Performance keeps chmbing. 123K views + 13 days ago

FY

t4

- , rai)

— PLES ION

slide-004.jpg

OCR text:

agents are also a thing ee

—— + i i ae See QE) SZ

et ee ee O.0}

3 7 Ce mren gn ts sae et se ("Ge -

7 @ sravegors witbe

' | _ wn a a

_ | | : ie meee °

ES

, i a Microsoft Qe?

~ >.

slide-005.jpg

OCR text:

they’re kinda the same thing actually

uve = om I hae

= | | |

7 “Sg a - eae _ ,

: - [a 4 =

rr

| aws

: |

slide-006.jpg

OCR text:

.

SFT warmup + small models = fun on just a few GPUs

as ‘ St ee |

results = vf_env.evaluatet S cares)

clientectient, ,

eodel=aodel_nane, He

sanpting_args:saapling_args,

Nus_samples=num_sanptes . i

) ,

hub _sodel_id="Qwen?. $-78-Math-Python-SFT™, _ -

}

trainer > SFTTrainert ;

mode l=nodel, : :

ergsrargs,

train dateset=dataset @ type: ignore Py

) . r

trainer. trarn()

“ aws

| a

x

wf me

Slide-Derived Subjects To Review

Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.