[pull] main from agentscope-ai:main

CONTRIBUTING.md

@@ -4,7 +4,7 @@ Thank you for your interest in Trinity-RFT! Our framework is built on a decouple
 
 ## Where to Contribute
 
-Trinity-RFT provides modular interfaces for different technical interests. Please refer to our [Developer Guide](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/develop_overview.html) for detailed implementation standards:
+Trinity-RFT provides modular interfaces for different technical interests. Please refer to our [Developer Guide](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/develop_overview.html) for detailed implementation standards:
 
 | Focus Area | Interface/Code Directory | Potential Tasks |
 | :--- | :--- | :--- |
@@ -41,10 +41,10 @@ To ensure a smooth review process, please complete the following:
 
 ## Additional Guidelines
 
-- **Bug Reports & Feature Requests**: Please use [GitHub Issues](https://github.com/modelscope/Trinity-RFT/issues). For bugs, include reproduction steps, environment info, and error logs.
+- **Bug Reports & Feature Requests**: Please use [GitHub Issues](https://github.com/agentscope-ai/Trinity-RFT/issues). For bugs, include reproduction steps, environment info, and error logs.
 - **Major Changes**: For significant architectural changes or large features, please open an issue first to discuss the design with the maintainers.
 - **Documentation**: We highly value improvements to our tutorials, docstrings, and translations.
 
-*For a deep dive into the framework's architecture, please refer to the [Full Doc](https://modelscope.github.io/Trinity-RFT/en/main/index.html).*
+*For a deep dive into the framework's architecture, please refer to the [Full Doc](https://agentscope-ai.github.io/Trinity-RFT/en/main/index.html).*
 
 **Thank you for helping us build a better Reinforcement Fine-Tuning framework!**

benchmark/README.md

@@ -70,7 +70,7 @@ python bench.py gsm8k --model_path /path/to/Qwen/Qwen2.5-1.5B-Instruct
 
 #### GSM8K Results
 
-The chart below shows performance based on this [commit](https://github.com/modelscope/Trinity-RFT/tree/068da409d215bb2450d93b6b7a56740d4751669d).
+The chart below shows performance based on this [commit](https://github.com/agentscope-ai/Trinity-RFT/tree/068da409d215bb2450d93b6b7a56740d4751669d).
 ![View Results](../docs/sphinx_doc/assets/gsm8k-bench.png)
 
 ### 2. Countdown
@@ -83,7 +83,7 @@ python bench.py countdown --model_path /path/to/Qwen/Qwen2.5-1.5B-Instruct
 
 #### Countdown Results
 
-The chart below shows performance based on this [commit](https://github.com/modelscope/Trinity-RFT/tree/068da409d215bb2450d93b6b7a56740d4751669d).
+The chart below shows performance based on this [commit](https://github.com/agentscope-ai/Trinity-RFT/tree/068da409d215bb2450d93b6b7a56740d4751669d).
 ![View Results](../docs/sphinx_doc/assets/countdown-bench.png)
 
 ### 3. Guru-Math
@@ -96,7 +96,7 @@ python bench.py guru_math --model_path /path/to/Qwen/Qwen2.5-7B
 
 #### Guru Results
 
-The chart below shows performance based on this [commit](https://github.com/modelscope/Trinity-RFT/tree/fbf6c967bcd637bfd9f81fb4d7dd4961d7d5a407).
+The chart below shows performance based on this [commit](https://github.com/agentscope-ai/Trinity-RFT/tree/fbf6c967bcd637bfd9f81fb4d7dd4961d7d5a407).
 ![View Results](../docs/sphinx_doc/assets/guru-bench.png)
 
 See [full report](./reports/guru_math.md) for details.
@@ -111,7 +111,7 @@ python bench.py frozen_lake --model_path /path/to/Qwen/Qwen2.5-3B
 
 #### Frozen Lake Results
 
-The chart below shows performance based on this [commit](https://github.com/modelscope/Trinity-RFT/tree/3861859cbd9c40de07429db2d9b19fd3d4d31703).
+The chart below shows performance based on this [commit](https://github.com/agentscope-ai/Trinity-RFT/tree/3861859cbd9c40de07429db2d9b19fd3d4d31703).
 ![View Results](../docs/sphinx_doc/assets/bench_frozenlake_step.png)
 
 See [full report](./reports/frozenlake.md) for details.
@@ -122,7 +122,7 @@ Please follow the instructions in [Alfworld report](./reports/alfworld.md) to ru
 
 #### ALFWorld Results
 
-The chart below shows performance based on this [commit](https://github.com/modelscope/Trinity-RFT/tree/3861859cbd9c40de07429db2d9b19fd3d4d31703).
+The chart below shows performance based on this [commit](https://github.com/agentscope-ai/Trinity-RFT/tree/3861859cbd9c40de07429db2d9b19fd3d4d31703).
 ![View Results](../docs/sphinx_doc/assets/bench_alfworld_step.png)
 
 

benchmark/reports/alfworld.md

@@ -10,11 +10,11 @@ The environment is configured as follows:
 * Reward Structure: +1 for successfully completing the task, -0.1 otherwise
 * Maximum Steps: 30 (configurable via `max_env_steps`)
 
-See the [documentation](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_multi_turn.html) for data preparation.
+See the [documentation](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/example_multi_turn.html) for data preparation.
 
 ## 2. Experimental Settings
 
-We evaluate the performance of the following methods in Trinity-RFT framework with version [0.3.3](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.3.3) (verl==0.5.0, vllm==0.11.0) and compare against the latest release of rLLM with commit ID [ef6451f](https://github.com/rllm-org/rllm/commit/ef6451fbd7eba224c4a87e3fd944d7c0e2bcc0ea) (verl==0.5.0) as of Nov. 6, 2025.
+We evaluate the performance of the following methods in Trinity-RFT framework with version [0.3.3](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.3.3) (verl==0.5.0, vllm==0.11.0) and compare against the latest release of rLLM with commit ID [ef6451f](https://github.com/rllm-org/rllm/commit/ef6451fbd7eba224c4a87e3fd944d7c0e2bcc0ea) (verl==0.5.0) as of Nov. 6, 2025.
 Since rLLM does not support ALFWorld environment yet, we implement this task in rLLM for comparison.
 
 In Trinity-RFT and rLLM, we respectively evaluate the performance using GRPO algorithm on this task.

benchmark/reports/frozenlake.md

@@ -21,7 +21,7 @@ To filter the unsolvable tasks, we restrict the game map to have a valid path wi
 
 ## 2. Experimental Settings
 
-We evaluate the performance of the following methods in Trinity-RFT framework with version [0.3.3](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.3.3) (verl==0.5.0, vllm==0.11.0) and compare against the latest release of rLLM with commit ID [ef6451f](https://github.com/rllm-org/rllm/commit/ef6451fbd7eba224c4a87e3fd944d7c0e2bcc0ea) (verl==0.5.0) as of Nov. 6, 2025.
+We evaluate the performance of the following methods in Trinity-RFT framework with version [0.3.3](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.3.3) (verl==0.5.0, vllm==0.11.0) and compare against the latest release of rLLM with commit ID [ef6451f](https://github.com/rllm-org/rllm/commit/ef6451fbd7eba224c4a87e3fd944d7c0e2bcc0ea) (verl==0.5.0) as of Nov. 6, 2025.
 
 We fine-tune a Qwen2.5-3B-Instruct model using the training tasks with GRPO. For all experiments, we fix key parameters to `batch_size=64`, `repeat_times=8`, and `lr=1e-6`. We run each experiment for three times and report the average results.
 

benchmark/reports/guru_math.md

@@ -6,7 +6,7 @@ Guru-Math is the mathematics task derived from the [Guru](https://huggingface.co
 
 ## 2. Experimental Settings
 
-We evaluate the performance of the following methods within the Trinity-RFT framework using version [0.3.3](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.3.3) (verl==0.5.0, vllm==0.10.2). For comparison, we ported relevant code from [Reasoning360](https://github.com/LLM360/Reasoning360) to be compatible with verl==0.5.0.
+We evaluate the performance of the following methods within the Trinity-RFT framework using version [0.3.3](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.3.3) (verl==0.5.0, vllm==0.10.2). For comparison, we ported relevant code from [Reasoning360](https://github.com/LLM360/Reasoning360) to be compatible with verl==0.5.0.
 
 Within both Trinity-RFT and veRL, we evaluate performance using the GRPO algorithm on this task. We fine-tune a base `Qwen2.5-7B` model that has not undergone prior fine-tuning.
 

docs/sphinx_doc/source/conf.py

@@ -90,7 +90,7 @@ def get_recent_tags(n: int) -> list:
     "article_header_end": "article_header_customized.html",
     "use_download_button": True,
     "use_fullscreen_button": True,
-    "repository_url": "https://github.com/modelscope/Trinity-RFT",
+    "repository_url": "https://github.com/agentscope-ai/Trinity-RFT",
     "use_repository_button": True,
 }