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--- ml:reinforcement_learning [2025/07/14 05:36] – jmflanig
+++ ml:reinforcement_learning [2025/07/14 05:40] (current) – [Reinforcement Learning with Verifiable Rewards] jmflanig
@@ Line 28: / Line 28: @@
     * [[https://aclanthology.org/2024.findings-emnlp.429.pdf|Wang et al 2024 - Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision]]
     * [[https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf|DeepSeek 2025 - DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning]]
+===== NLP RL Papers =====
+(Some of the papers above should be moved to this section)
+  * **Applied to Text Games**
+    * [[https://arxiv.org/pdf/1506.08941|Narasimhan et al 2015 - Language Understanding for Text-based Games using Deep Reinforcement Learning]]
 ===== Reinforcement Learning with Verifiable Rewards =====
 DeepSeek-R1-Zero-style reinforcement learning is sometimes called **"reinforcement learning (RL) on verifiable rewards"** (see for example [[https://arxiv.org/pdf/2505.21493|Zhou 2025]]) or **"RL with outcome supervision."**
+See also [[nlp:Large Reasoning Models]]
     * [[https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf|DeepSeek 2025 - DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning]]
     * [[https://arxiv.org/pdf/2505.21493|Zhou et al 2025 - Reinforcing General Reasoning without Verifiers]]
-===== NLP Papers =====
-(Some of the papers above should be moved to this section)
-  * [[https://arxiv.org/pdf/1506.08941|Narasimhan et al 2015 - Language Understanding for Text-based Games using Deep Reinforcement Learning]]
 ===== Datasets =====