MaD-RL: Matching Distributions for Calibrating LLMs with Reinforcement Learning

arXiv:2609.31644 · cs.LG, cs.AI · Submitted 2026-09-12 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-12

Updated: 2026-09-12

Terminology

• Accuracy — 19× in this paper · explained in 1 episode
• Distribution Matching — 12× in this paper · explained in 2 episodes
• GRPO — 12× in this paper · explained in 6 episodes
• Reinforcement Learning — 9× in this paper · explained in 4 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Steering — 4× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Verifier — 3× in this paper · explained in 2 episodes
• Actor — 2× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Catastrophic Forgetting — 2× in this paper · explained in 5 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 2× in this paper
• Jensen–Shannon Divergence (JSD) — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Self-consistency — 2× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Group Relative Policy Optimization — 1× in this paper
• Group Relative Policy Optimization (GRPO) — 1× in this paper · explained in 5 episodes
• In International Conference on Machine Learning — 1× in this paper
• Inference-Time Steering — 1× in this paper · explained in 2 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Score Function — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verifiable Rewards — 1× in this paper · explained in 2 episodes

Sources

• Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
• CTRL: A Conditional Transformer Language Model for Controllable Generation
• Tulu 3: Pushing Frontiers in Open Language Model Post-Training
• Proximal Policy Optimization Algorithms
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Inverse RL Helps Align AI by Imitating Humans

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks