A benchmark for evaluating realistic preference-following in personalized user-LLM interactions.

OpenClaw-RL: Train any agent simply by talking

PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory

Reinforcement Learning via Self-Distillation (SDPO)

A MemAgent framework that can be extrapolated to 3.5M, along with a training framework for RL training of any agent workflow.

The official implementation of the paper "Mem-α: Learning Memory Construction via Reinforcement Learning"

Code and Data for Tau-Bench

Training Proactive and Personalized LLM Agents

Baselines for personalized RLHF methods including GPO, DPO, and various reward modeling approaches

[NeurIPS 2025 D&B] Open-source Multi-agent Poster Generation from Papers

Official Repo for Open-Reasoner-Zero

Code and data for "Inferring Rewards from Language in Context" [ACL 2022].

The official repo for the code and data of paper SMART

Official Implementation of "DeLLMa: Decision Making Under Uncertainty with Large Language Models"

DialOp: Decision-oriented dialogue environments for collaborative language agents

Extended Inductive Reasoning for Personalized Preference Inference from Behavioral Signals

[ICLR 2025] No Preference Left Behind: Group Distributional Preference Optimization

Benchmarking Chat Assistants on Long-Term Interactive Memory (ICLR 2025)

[ACL 2025 Demo] Repository for the demo and paper: ReasonGraph: Visualisation of Reasoning Paths

[NeurIPS 2024] Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models

[NeurIPS D&B '25] The one-stop repository for LLM unlearning

Framework and toolkits for building and evaluating collaborative agents that can work together with humans.

[EMNLP 2024] Ask-before-Plan: Proactive Language Agents for Real-World Planning