Add link to paper page

README.md

@@ -1,24 +1,24 @@
 ---
-license: apache-2.0
 base_model: Qwen/Qwen3-8B
-pipeline_tag: text-generation
-library_name: transformers
 language:
-  - en
+- en
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
 tags:
-  - spyrl
-  - rlsvr
-  - self-play
-  - reinforcement-learning
-  - mathematical-reasoning
-  - reasoning
+- spyrl
+- rlsvr
+- self-play
+- reinforcement-learning
+- mathematical-reasoning
+- reasoning
 ---
 
 # SpyRL-Qwen3-8B-Math
 
 Qwen3-8B trained with SpyRL on mathematical reasoning. In the performing stage each agent designs *and* solves a problem grounded in a math-heavy web document; the spy sees the same document with a contiguous 40% span masked out.
 
-Trained with **SpyRL**, the reference implementation of **RLSVR** (Reinforcement Learning with Self-Verifiable Rewards) from the COLM 2026 paper *From RLVR to RLSVR: Task Transformation Induces Self-Verifiable Rewards for Open-Ended LLM Self-Improvement*.
+Trained with **SpyRL**, the reference implementation of **RLSVR** (Reinforcement Learning with Self-Verifiable Rewards) from the COLM 2026 paper [*From RLVR to RLSVR: Task Transformation Induces Self-Verifiable Rewards for Open-Ended LLM Self-Improvement*](https://huggingface.co/papers/2607.23802).
 
 RLSVR extends RLVR to open-ended tasks the way self-supervised learning extends supervised learning: instead of approximating a missing reward with a judge or reward model, it *transforms* the task into a proxy environment whose own rules generate the reward. SpyRL instantiates that as a multi-agent self-play game inspired by *Who Is the Spy?* — civilians receive the full input, one spy receives a masked copy, all agents perform the same target task, and then they vote on who the spy is. Because the environment assigns the spy identity up front, the vote is exactly checkable, and avoiding suspicion requires producing genuinely better output.
 
@@ -89,4 +89,4 @@ bash spyrl/train_math_reasoning.sh
     booktitle = {Conference on Language Modeling (COLM)},
     year      = {2026}
 }
-```
+```