Add metadata and links to paper/code (#1)

- Add metadata and links to paper/code (c95740e4f369cb99892a3915dc15dc4156dbd70a)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,17 +1,26 @@
 ---
 license: mit
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- long-context
+- reinforcement-learning
+- rlvr
+- grpo
+- multitask
 ---
+
 # ✨ GoLongRL-4B
 
-We present GoLongRL, a fully open-source, capability-oriented post-training recipe for long-context reinforcement learning with verifiable rewards (RLVR).
+We present **GoLongRL**, a fully open-source, capability-oriented post-training recipe for long-context reinforcement learning with verifiable rewards (RLVR).
 
 | Resource | Link |
 |---|---|
-| 📝 Preprints | [Paper](https://arxiv.org/abs/2605.19577) |
-| 🤗 Daily Paper | [Paper]() |
-| 🤗 Model Hub | [GoLongRL-4B](https://huggingface.co/Kwai-Klear/GoLongRL-4B) |
-| 🤗 Model Hub | [GoLongRL-30B-A3B](https://huggingface.co/Kwai-Klear/GoLongRL-30B-A3B) |
-| 🤗 Dataset Hub | [GolongRL Dataset](https://huggingface.co/datasets/Kwai-Klear/GoLongRL) |
+| 📝 Paper | [ArXiv (2605.19577)](https://arxiv.org/abs/2605.19577) |
+| 🤗 HF Paper | [GoLongRL](https://huggingface.co/papers/2605.19577) |
+| 💻 Code | [GitHub Repository](https://github.com/xiaoxuanNLP/GoLongRL) |
+| 📂 Collection | [GoLongRL Collection](https://huggingface.co/collections/Kwai-Klear/golongrl) |
+| 📊 Dataset | [GoLongRL Dataset](https://huggingface.co/datasets/Kwai-Klear/GoLongRL) |
 | 📧 Contact | xiao_xuan_zi_666@163.com & suzhenpeng13@163.com |
 
 ## 📌 Overview
@@ -22,31 +31,20 @@ We present GoLongRL, a fully open-source, capability-oriented post-training reci
 <sub>Overall performance comparison on long-context benchmarks (DocMath, LongBench-V2, Frames, MRCR, CorpusQA, LBV1-QA).</sub>
 </div>
 
-**GoLongRL-4B** achieves strong long-context performance at the 4B scale.
-
-| Model | Avg. | DocMath | LBV2 | Frames | MRCR | CorpusQA | LBV1-QA |
-|---|---|---|---|---|---|---|---|
-| Qwen3-4B-Thinking-2507 | 53.0 | 61.0 | 40.2 | 64.4 | 38.4 | 49.9 | 64.0 |
-| QwenLong-L1.5-4B (w. GRPO) | 56.1 | 61.3 | 44.3 | 67.1 | 40.9 | 58.8 | 64.1 |
-| QwenLong-L1.5-4B (w. AEPO) | 59.4 | 62.5 | 47.9 | 67.4 | 47.9 | 64.7 | 65.8 |
-| GoLongRL-4B (w. GRPO) | 62.2 | 62.5 | 45.5 | 66.6 | 67.5 | 65.1 | 65.9 |
-| **GoLongRL-4B (w. TMN-Reweight, Ours)** | **63.0** | **62.3** | **47.1** | **67.4** | **65.5** | **69.6** | **65.9** |
-
-Our framework combines the following.
+**GoLongRL-4B** achieves strong long-context performance at the 4B scale. Our framework combines the following:
 
-1. **Capability-Oriented Dataset (23K samples, 9 task types).** Guided by a taxonomy of long-context capabilities, the dataset covers precise retrieval, comprehension, exhaustive retrieval, numerical reasoning, structured extraction, structured matching, graded ranking, sequence ordering, and summarization. Each task is paired with its natural evaluation metric (EM, Accuracy, F1, math_verify, IoU, SubEM, NDCG, Pairwise, ROUGE-L) as the reward function, rather than being collapsed into a single indicator.
+1. **Capability-Oriented Dataset (23K samples, 9 task types).** Guided by a taxonomy of long-context capabilities, the dataset covers precise retrieval, comprehension, exhaustive retrieval, numerical reasoning, structured extraction, structured matching, graded ranking, sequence ordering, and summarization. Each task is paired with its natural evaluation metric (EM, Accuracy, F1, math_verify, IoU, SubEM, NDCG, Pairwise, ROUGE-L) as the reward function.
 
-2. **TMN-Reweight.** When training on heterogeneous reward types, per-prompt normalization in standard GRPO can mix up cross-task scale differences with prompt difficulty. TMN-Reweight is a simple modification that normalizes advantages at the task level instead of the prompt level, and adds a difficulty-adaptive weight to reduce noise from very easy or very hard prompts. It provides a modest but consistent improvement over vanilla GRPO in our ablations (+0.8 avg. at 4B scale), with gains mainly on aggregation-intensive benchmarks like CorpusQA.
+2. **TMN-Reweight.** To address optimization challenges from heterogeneous rewards, we propose TMN-Reweight, which combines task-level mean normalization for cross-task reward scale alignment with difficulty-adaptive weighting for more reliable advantage estimation. It provides a modest but consistent improvement over vanilla GRPO.
 
 3. **Full Open Release.** We publicly release the complete dataset, the four-phase construction pipeline, and all training code.
 
 ### Key Results
 
-- Under the same vanilla GRPO setup, our dataset alone outperforms the closed-source QwenLong-L1.5 dataset at both 4B and 30B scales (+6.1 avg. at 4B, +2.6 avg. at 30B).
-- TMN-Reweight further improves average performance to 63.0 at 4B scale, surpassing QwenLong-L1.5 with its specialized AEPO algorithm (59.4).
+- Under the same vanilla GRPO setup, our dataset alone outperforms the closed-source QwenLong-L1.5 dataset.
+- TMN-Reweight further improves average performance to 63.0 at 4B scale, surpassing QwenLong-L1.5 with its specialized AEPO algorithm.
 - General capabilities (MMLU-Pro, AIME24/25, GPQA) are preserved or improved, with substantial gains on dialogue memory (LongMemEval +13.6) and agentic memory benchmarks.
 
-
 ## 🔍 Evaluation
 
 Evaluation uses **QwenLong-Benchmarks**, covering three capability dimensions:
@@ -57,10 +55,9 @@ Evaluation uses **QwenLong-Benchmarks**, covering three capability dimensions:
 | **General** | MMLU-Pro, AIME 2024/2025, GPQA-Diamond |
 | **Memory** | BFCL-V4 (memory subset), LongMemEval |
 
-
 ## 🤝 Citation
 
-```
+```bibtex
 @misc{lv2026golongrlcapabilityorientedlongcontext,
       title={GoLongRL: Capability-Oriented Long Context Reinforcement Learning with Multitask Alignment}, 
       author={Minxuan Lv and Tiehua Mei and Tanlong Du and Junmin Chen and Zhenpeng Su and Ziyang Chen and Ziqi Wang and Zhennan Wu and Ruotong Pan and jian Liang and Ruiming Tang and Han Li},