Categories: FAANG

Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning

This paper was accepted at the workshop at “Human-in-the-Loop Learning Workshop” at NeurIPS 2022.
Preference-based reinforcement learning (RL) algorithms help avoid the pitfalls of hand-crafted reward functions by distilling them from human preference feedback, but they remain impractical due to the burdensome number of labels required from the human, even for relatively simple tasks. In this work, we demonstrate that encoding environment dynamics in the reward function (REED) dramatically reduces the number of preference labels required in state-of-the-art preference-based RL frameworks. We…

Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment

Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences because standard post-training methods, like Reinforcement Learning with Human Feedback (RLHF), optimize for a single, global objective. While Group Relative Policy Optimization (GRPO) is a widely adopted on-policy reinforcement learning framework, its group-based…

April 3, 2026

In "FAANG"

Rethinking the Role of PPO in RLHF

October 17, 2023

In "AI/ML Research"

Interleaved Reasoning for Large Language Models via Reinforcement Learning

Long chain-of-thought (CoT) significantly enhances large language models' (LLM) reasoning capabilities. However, the extensive reasoning traces lead to inefficiencies and an increased time-to-first-token (TTFT). We propose a novel training paradigm that uses reinforcement learning (RL) to guide reasoning LLMs to interleave thinking and answering for multi-hop questions. We observe that…

May 29, 2025

In "FAANG"

AI Generated Robotic Content