Who doesn’t love good methods for reward inference. What if I told you that you could extract dense rewards from video, by ranking frames temporally using the BT model from RLHF (aka just doing temporal classification with cross-entropy). Let's see how, in rank2reward - a🧵(1/10)

#Claude3 explains about RLHF and ethical principles

Self-Play Preference Optimization for Language Model Alignment

Traditional reinforcement learning from human feedback (RLHF) approaches relying on parametric models like the Bradley-Terry model fall short in capturing the intransitivity and irrationality in human preferences.

In Vienna for two 🔦 spotlights at #ICLR2024

🕵MuSR by Zayne Sprague @ ICLR 24 +al, Tues 10:45am

🧑‍💻✍️ Coeditor by Jiayi Wei with Isil Dillig , Thurs 10:45am (presented by me)

DM me if you're interested in chatting about these, reasoning + factuality in LLMs, RLHF, or other topics!

RLHF research requires training and hiring annotators to explicitly choose between different model outputs.

What if we can get human preference based on user edits, which are naturally generated in applications like AI writing assistants? arxiv.org/abs/2404.15269

🥳Our paper 'Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint' is accepted by ICML2024 (also an ORAL presentation at ICLR ME-FoMo workshop)!

- Problem formulation: we first formally formulate the RLHF as a…

I’ll be at #ICLR2024 on Saturday for the LLM Agents Workshop Panel 🚀

Some of my collaborators will also be there throughout the week presenting our work on:
- how RLHF affects LLM generalisation and diversity with Robert Kirk @ ICLR 2024
- training NetHack agents using LLM feedback with…

Interested in language model finetuning? Stop by our poster Wednesday morning at #ICLR2024 to hear about a vanishing gradients problem of RLHF!

At the same session, Hattie Zhou ✈️ ICLR will present a fascinating work on understanding what algorithms Transformers can learn

Experimenting with some PPO / chat recipes. I noticed there is always a drop off in RLHF reward initially (`(score.mean() - per_token_kl.sum(1).mean())`). Do people observe similar phenomena?

Scott Emmons discussed at #TAIS2024 the issues of partial observability in reinforcement learning from human feedback (RLHF). He challenged the prevalent notion that human evaluators have complete awareness of the environment when providing feedback. Scott revealed that under…

Introducing “Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo”

Many capability and safety techniques of LLMs—such as RLHF, automated red-teaming, prompt engineering, and infilling—can be viewed from a probabilistic inference perspective, specifically…

🌎New Video🌏
Explaining ORPO: Monolithic Preference Optimization without Reference Model - a more stable, less costly, better performing, and single-step alternative to SFT + RLHF / DPO
Watch here: youtu.be/52kMBrAI_IM

RLHF/DPO小話 最終回（その4）を執筆しました。
最後までお読みいただいた方ありがとうございました！
akifumi-wachi-4.github.io/website/column…

ところで、私がメンターのインターン募集です↓
lycorp.co.jp/ja/recruit/lan…

[LG] DPO Meets PPO: Reinforced Token Optimization for RLHF
arxiv.org/abs/2404.18922
- This paper models RLHF as an MDP, offering a token-wise characterization of LLM's generation process. It theoretically demonstrates advantages of token-wise MDP over sentence-wise bandit…

Self-Play Preference Optimization for Language Model Alignment

SPPO serves as the RLHF counterpart of SPIN and outperforms iterative DPO, Snorkel AI, Self-Rewarding LM, GPT-4 0613 etc

arxiv.org/abs/2405.00675

Why Human Feedback?
Learning from Human Preferences

Reinforcement learning from human feedback (RLHF)

The article provides a detailed overview of reinforcement learning from human feedback (RLHF), emphasizing its integration with human-computer interaction to refine AI…

αιamblichus j⧉nus RLHF runs deep

GPT '3.5' was the biggest psyop in AI – starting with the very labeling. In retrospect it's clear that the LLM behind ChatGPT had zero relation to the davinci 175B@500B series and was more like overtrained late 2023 models. But for a year+, we had to debate the Magic Of RLHF.

DPO Meets PPO: Reinforced Token Optimization for RLHF
youtu.be/u8Cg07NxELM

'Numerous capability and safety techniques of Large Language Models (LLMs), including RLHF, automated red-teaming, prompt engineering, and infilling, can be cast as sampling from an unnormalized target distribution defined by a given reward or potential function over the full…