arXiv AI
Jul 7
Adaptive Margin RLHF via Preference over Preferences
arXiv:2509. 22851v4 Announce Type: replace-cross Abstract: Margin-based optimization is fundamental to improving generalization and robustness in classification tasks.
Value-Free Policy Optimization via Reward Partitioning
arXiv:2506. 13702v4 Announce Type: replace-cross Abstract: Single-trajectory preference optimization methods learn from datasets of ((prompt, response, reward)) tuples, offering a practical alternative to pairwise preference learning by directly leveraging scalar feedback.
Related stories
arXiv AI
Sep 1
Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment
The paper introduces Personalized Group Relative Policy Optimization (P‑GRPO), a new alignment framework for large language models that separates advantage estimation from immediate batch statistics. By normalizing advantages using preference‑group‑specific reward histories instead of the concurrent generation group, P‑GRPO maintains contrastive signals for distinct user preferences. Experiments across various tasks show that P‑GRPO converges faster and yields higher rewards than standard GRPO, improving alignment with heterogeneous human preferences while preserving general capabilities.
arXiv AI
Jul 7
RSPO: Reward-Swap Policy Optimization for Multi-Turn LLM Agents
arXiv:2607. 04713v1 Announce Type: cross Abstract: Reinforcement learning holds significant potential for training large language models (LLMs) to handle multi-turn interactive tasks.
arXiv Machine Learning
1d ago
Range-GRPO: Policy Optimization via Pairwise Relations among Reward Intervals
Range-GRPO introduces a semi‑supervised post‑training framework that uses conformally calibrated reward ranges instead of single point scores for large language models. By comparing reward ranges pairwise within rollout groups, the method incorporates reward uncertainty into both the magnitude and direction of learning signals. Experiments show that Range‑GRPO outperforms other semi‑supervised approaches on both in‑distribution and out‑of‑distribution tasks while using fewer training resources.
arXiv AI
Jun 30
Distributionally Robust Reinforcement Learning with Human Feedback
arXiv:2503. 00539v2 Announce Type: replace-cross Abstract: Reinforcement learning from human feedback (RLHF) has evolved to be one of the main methods for fine-tuning large language models (LLMs).
arXiv AI
Jun 4
BiasGRPO: Stabilizing Bias Mitigation in High-Variance Reward Landscapes via Group-Relative Policy Optimization
arXiv:2606. 04807v1 Announce Type: new Abstract: Mitigating social bias in Large Language Models (LLMs) presents a distinct alignment challenge: unlike verifiable tasks, bias lacks a single ground truth, creating a high-variance, subjective reward landscape.
arXiv AI
Aug 3
Don't Mix Rewards, Mix Policies: Policy Decomposition and Optimization for Multi-Reward RL
arXiv:2607. 29246v1 Announce Type: new Abstract: Modern large language models (LLMs) are expected not just to answer correctly, but to adapt their behavior to different human values and use cases.
arXiv Machine Learning
Jul 31
S-GRPO: Unified Post-Training for Large Vision-Language Models
arXiv:2604. 16557v2 Announce Type: replace Abstract: Current post-training methodologies for adapting Large Vision-Language Models (LVLMs) generally fall into two paradigms: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL).
arXiv AI
Jun 15
GAGPO: Generalized Advantage Grouped Policy Optimization
arXiv:2605. 13217v1 Announce Type: cross Abstract: Reinforcement learning has become a powerful paradigm for post-training large language model agents, yet credit assignment in multi-turn environments remains a challenge.
arXiv AI
4d ago
Group-Marginalized Self-Rewarding RL Drives Zero-Label Self-Evolving
arXiv:2609.36750v1 Announce Type: cross Abstract: Self-rewarding reinforcement learning (RL) enables large language models (LLMs) to self-evolve without human labels. Existing ensemble-based methods...
arXiv AI
Jul 1
Freeform Preference Learning for Robotic Manipulation
arXiv:2606. 32027v1 Announce Type: cross Abstract: Reward design remains a central bottleneck for autonomous robot policy improvement, especially in long-horizon manipulation tasks where sparse success labels provide too little signal and binary preferences collapse many competing notions of quality into one ambiguous signal.
arXiv AI
Sep 15
Specifying Reward Functions for RL Without Environment Sampling
arXiv:2609.15544v1 Announce Type: cross Abstract: Enabling human stakeholders to specify reward functions that lead to their desired outcomes is a key challenge in deploying reinforcement learning ag...