There's growing evidence that LLMs can p-hack. But p-hacking also points to something bigger: a data science multiverse of defensible analytical choices. We wrote a paper (arxiv.org/abs/2602.18710) on using LLM agents to map this multiverse systematically. 🧵

I was lucky enough to be invited give a talk on our new paper on the value of RL in fine-tuning at Cornell last week! Because of my poor time management skills, the talk isn't as polished as I'd like, but I think the "vibes" are accurate enough to share: youtu.be/E4b3cSirpsg.

All Roads Lead to Likelihood: The Value of RL in Fine-Tuning

YouTube video by Gokul Swamy

youtu.be

Gokul Swamy@gokul.dev · last yr.

1.5 yrs ago, we set out to answer a seemingly simple question: what are we *actually* getting out of RL in fine-tuning? I'm thrilled to share a pearl we found on the deepest dive of my PhD: the value of RL in RLHF seems to come from *generation-verification gaps*. Get ready to 🤿:

1.5 yrs ago, we set out to answer a seemingly simple question: what are we *actually* getting out of RL in fine-tuning? I'm thrilled to share a pearl we found on the deepest dive of my PhD: the value of RL in RLHF seems to come from *generation-verification gaps*. Get ready to 🤿:

@gswamy.bsky.social et al propose SPO which builds a game from a preferences, solving for the minimax winner. Handles non-Markovian, intransitive, and stochastic preferences. Nice empirical eval ranging from small demonstrative domains to huge RL domain (Mujoco). arxiv.org/abs/2401.04056 2/3.

A Minimaximalist Approach to Reinforcement Learning from Human Feedback

We present Self-Play Preference Optimization (SPO), an algorithm for reinforcement learning from human feedback. Our approach is minimalist in that it does not require training a reward model nor unst...

arxiv.org

I have become a fan of the game-theoretic approaches to RLHF, so here are two more papers in that category! (with one more tomorrow 😅) 1. Self-Play Preference Optimization (SPO). 2. Direct Nash Optimization (DNO). 🧵 1/3.

Marc Lanctot@sharky6000.bsky.social · 2y ago

Last week, I shared some papers in the intersection of agent/model evaluation and social choice theory. The last was a position paper on RLHF/alignment. This week I will share papers (in pairs) on the topic of "game-theoretic or social choice meet meet alignment/RLHF". 🧵 1/3.