arXiv AI
LaRA: Layer-wise Representation Analysis for Detecting Data Contamination in RL Post-Training
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arXiv Computation and Language
Oct 16
Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models
Composition‑RL is an offline data‑augmentation method that composes existing prompts before reinforcement learning to address the pass‑rate‑1 bottleneck in large‑language‑model training. By generating and verifying a composed dataset once from a fixed source pool, it avoids modifying the RL optimizer and improves reasoning performance across models ranging from 4B to 30B. A curriculum variant that gradually increases compositional depth further boosts performance, and the approach also facilitates cross‑domain RL by combining prompts from different domains.
arXiv Machine Learning
Aug 27
Demystifying Reinforcement Learning Post-Training of Language Models
The paper "Demystifying Reinforcement Learning Post-Training of Language Models" investigates how reinforcement learning (RL) post‑training enhances large language models (LLMs) for tasks such as reasoning, math, and coding. By isolating RL components in a controlled setting, the authors analyze how the base model’s prior distribution, reward granularity, prompt diversity, and model scale influence outcomes, using policy entropy to compare pre‑training, supervised fine‑tuning (SFT), and RL stages. The study clarifies the role of spurious rewards, the importance of the base model’s probability mass on desired behaviors, and how these factors interact to determine post‑training success, offering a practical primer for NLP researchers. "whyItMatters":"The work provides a clearer understanding of RL post‑training mechanics, helping researchers and practitioners effectively apply RL to improve LLM capabilities."
arXiv AI
Jun 4
GeoMin: Data-Efficient Semi-Supervised RLVR via Geometric Distribution Modeling
arXiv:2606. 04516v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) significantly advances LLM reasoning, yet it faces a dilemma: standard supervised scaling is throttled by high annotation costs, while unsupervised alternatives suffer from severe model collapse.
arXiv Machine Learning
Jul 31
Noisy Data is Destructive to Reinforcement Learning with Verifiable Rewards
arXiv:2603. 16140v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven recent capability advances of large language models across various domains.
arXiv AI
Oct 2
On Language Drift during RLVR Post-Training
arXiv:2610.02015v1 Announce Type: cross Abstract: Recent advances in LLM reasoning models---driven primarily by the paradigm of post-training via reinforcement learning with verifiable reward (RLVR)-...
arXiv AI
4d ago
Taming CoT Obfuscation in VLMs: From Mechanistic Evidence to Activation-Level Enforcement
arXiv:2609.24243v3 Announce Type: replace Abstract: Reinforcement learning (RL) has substantially improved the reasoning capabilities of vision-language models (VLMs), yet it can induce chain-of-thou...