Intelligence with Everyone: RL @ MiniMax, with Olive Song, from AIE NYC & Inference by Turing Post

About this episode

Olive Song from MiniMax shares how her team trains the M series frontier open-weight models using reinforcement learning, tight product feedback loops, and systematic environment perturbations. This crossover episode weaves together her AI Engineer Conference talk and an in-depth interview from the Inference podcast. Listeners will learn about interleaved thinking for long-horizon agentic tasks, fighting reward hacking, and why they moved RL training to FP32 precision. Olive also offers a candid look at debugging real-world LLM failures and how MiniMax uses AI agents to track the fast-moving AI landscape.

Use the Granola Recipe Nathan relies on to identify blind spots across conversations, AI research, and decisions: https://bit.ly/granolablindspot

LINKS:

Conference Talk (AI Engineer, Dec 2025) – https://www.youtube.com/watch?v=lY1iFbDPRlw
Interview (Turing Post, Jan 2026) – https://www.youtube.com/watch?v=GkUMqWeHn40

Sponsors:

Claude:

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Tasklet:

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CHAPTERS:

(00:00) About the Episode

(04:15) Minimax M2 presentation (Part 1)

(17:59) Sponsors: Claude | Tasklet

(21:22) Minimax M2 presentation (Part 2)

(21:26) Research life and culture

(26:27) Alignment, safety and feedback

(32:01) Long-horizon coding agents

(35:57) Open models and evaluation

(43:29) M2.2 and researcher goals

(48:16) Continual learning and AGI

(52:58) Closing musical summary

(55:49) Outro

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