Post-training — page 3
- Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models
arxiv:2601.18734· 2026-01-26 · text available - Self-Refine: Iterative Refinement with Self-Feedback
arxiv:2303.17651· 2023-03-30 · text available - Single-stream Policy Optimization
arxiv:2509.13232· 2025-09-16 · text available - Skip-Connected Policy Optimization for Implicit Advantage
arxiv:2604.08690· 2026-04-09 · text available - Soft Adaptive Policy Optimization
arxiv:2511.20347· 2025-11-25 · text available - Spurious Rewards Paradox: Mechanistically Understanding How RLVR Activates Memorization Shortcuts in LLMs
arxiv:2601.11061· 2026-01-16 · text available - SR-GRPO: Stable Rank as an Intrinsic Geometric Reward for Large Language Model Alignment
arxiv:2512.02807· 2025-12-02 · text available - The Art of Scaling Reinforcement Learning Compute for LLMs
arxiv:2510.13786· 2025-10-15 · text available - The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models
arxiv:2503.02875· 2025-03-04 · text available - To Retain or to Adapt? Generalizing Continual Learning
arxiv:2607.05609· 2026-07-06 · text available - Towards Execution-Grounded Automated AI Research
arxiv:2601.14525· 2026-01-20 · text available - TTPO: Test-Time Policy Optimization
arxiv:2608.27448· 2026-08-27 · text available - TTRL: Test-Time Reinforcement Learning
arxiv:2504.16084· 2025-04-22 · text available - Understanding R1-Zero-Like Training: A Critical Perspective
arxiv:2503.20783· 2025-03-26 · text available - Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing
arxiv:2604.02288· 2026-04-02 · text available - Unsupervised Elicitation of Language Models
arxiv:2506.10139· 2025-06-11 · text available - Weight-Space Geometry of Offline Reasoning Training
arxiv:2606.23740· 2026-06-26 · text available - When Does Continual Learning Require Learning
arxiv:2607.07847· 2026-07-08 · text available - Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs?
arxiv:2603.24472· 2026-03-25 · text available