PoEM: Predicting RL Outcomes from Existing Policies
cs.LG, cs.AI, cs.CL, cs.CV
Submitted: 2026-09-24
Updated: 2026-09-24
Terminology
Sources
- Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models
- Successor Features for Transfer in Reinforcement Learning
- Training Diffusion Models with Reinforcement Learning
- InternLM2 Technical Report
- Bootstrapping Language Models with DPO Implicit Rewards
- Training Verifiers to Solve Math Word Problems
- Process Reinforcement through Implicit Rewards
- RLHF Workflow: From Reward Modeling to Online RLHF
- Editing Models with Task Arithmetic
- Datamodels: Predicting Predictions from Training Data
- Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging
- PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference
- Tulu 3: Pushing Frontiers in Open Language Model Post-Training
- Tuning Language Models by Proxy
- Decoding-time Realignment of Language Models
- AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling
- WebGPT: Browser-assisted question-answering with human feedback
- Training language models to follow instructions with human feedback
- Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
- Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap
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