Release v0.3.1 (#328)

Author: pan-x-c

README.md

@@ -60,8 +60,9 @@ Trinity-RFT is a flexible, general-purpose framework for reinforcement fine-tuni
 
 ## 🚀 News
 
-* [2025-09] ✨ [[Release Notes](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.3.0)] Trinity-RFT v0.3.0 released: enhanced Buffer, FSDP2 & Megatron support, multi-modal models, and new RL algorithms/examples.
-* [2025-08] 🎵 Introducing [CHORD](https://github.com/modelscope/Trinity-RFT/tree/main/examples/mix_chord): dynamic SFT + RL integration for advanced LLM fine-tuning ([paper](https://arxiv.org/pdf/2508.11408)).
+* [2025-10] ✨ [[Release Notes](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.3.1)] Trinity-RFT v0.3.1 released: multi-stage training support, improved agentic RL examples, LoRA support, debug mode and new RL algorithms.
+* [2025-09] [[Release Notes](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.3.0)] Trinity-RFT v0.3.0 released: enhanced Buffer, FSDP2 & Megatron support, multi-modal models, and new RL algorithms/examples.
+* [2025-08] Introducing [CHORD](https://github.com/modelscope/Trinity-RFT/tree/main/examples/mix_chord): dynamic SFT + RL integration for advanced LLM fine-tuning ([paper](https://arxiv.org/pdf/2508.11408)).
 * [2025-08] [[Release Notes](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.2.1)] Trinity-RFT v0.2.1 released.
 * [2025-07] [[Release Notes](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.2.0)] Trinity-RFT v0.2.0 released.
 * [2025-07] Technical report (arXiv v2) updated with new features, examples, and experiments: [link](https://arxiv.org/abs/2505.17826).
@@ -157,14 +158,14 @@ uv sync --extra dev --extra flash_attn
 If you just want to use the package without modifying the code:
 
 ```bash
-pip install trinity-rft==0.3.0
+pip install trinity-rft==0.3.1
 pip install flash-attn==2.8.1
 ```
 
 Or with `uv`:
 
 ```bash
-uv pip install trinity-rft==0.3.0
+uv pip install trinity-rft==0.3.1
 uv pip install flash-attn==2.8.1
 ```
 

README_zh.md

@@ -59,8 +59,9 @@ Trinity-RFT 是一个灵活、通用的大语言模型（LLM）强化微调（RF
 
 ## 🚀 新闻
 
-* [2025-09] ✨ [[发布说明](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.3.0)] Trinity-RFT v0.3.0 发布：增强的 Buffer、FSDP2 & Megatron 支持，多模态模型，以及全新 RL 算法/示例。
-* [2025-08] 🎵 推出 [CHORD](https://github.com/modelscope/Trinity-RFT/tree/main/examples/mix_chord)：动态 SFT + RL 集成，实现进阶 LLM 微调（[论文](https://arxiv.org/pdf/2508.11408)）。
+* [2025-10] ✨ [[发布说明](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.3.1)] Trinity-RFT v0.3.1 发布：多阶段训练支持、改进的智能体 RL 示例、LoRA 支持、调试模式和全新 RL 算法。
+* [2025-09] [[发布说明](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.3.0)] Trinity-RFT v0.3.0 发布：增强的 Buffer、FSDP2 & Megatron 支持，多模态模型，以及全新 RL 算法/示例。
+* [2025-08] 推出 [CHORD](https://github.com/modelscope/Trinity-RFT/tree/main/examples/mix_chord)：动态 SFT + RL 集成，实现进阶 LLM 微调（[论文](https://arxiv.org/pdf/2508.11408)）。
 * [2025-08] [[发布说明](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.2.1)] Trinity-RFT v0.2.1 发布。
 * [2025-07] [[发布说明](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.2.0)] Trinity-RFT v0.2.0 发布。
 * [2025-07] 技术报告（arXiv v2）更新，包含新功能、示例和实验：[链接](https://arxiv.org/abs/2505.17826)。
@@ -153,14 +154,14 @@ uv sync --extra dev --extra flash_attn
 如果您只需使用 Trinity-RFT 而不打算修改代码：
 
 ```bash
-pip install trinity-rft==0.3.0
+pip install trinity-rft==0.3.1
 pip install flash-attn==2.8.1
 ```
 
 或使用 `uv`：
 
 ```bash
-uv pip install trinity-rft==0.3.0
+uv pip install trinity-rft==0.3.1
 uv pip install flash-attn==2.8.1
 ```
 

docs/sphinx_doc/source/tutorial/trinity_installation.md

@@ -65,14 +65,14 @@ uv sync --extra dev --extra flash_attn
 If you just want to use the package without modifying the code:
 
 ```bash
-pip install trinity-rft==0.3.0
+pip install trinity-rft==0.3.1
 pip install flash-attn==2.8.1
 ```
 
 Or with `uv`:
 
 ```bash
-uv pip install trinity-rft==0.3.0
+uv pip install trinity-rft==0.3.1
 uv pip install flash-attn==2.8.1
 ```
 

docs/sphinx_doc/source_zh/tutorial/trinity_installation.md

@@ -65,14 +65,14 @@ uv sync --extra dev --extra flash_attn
 如果您只需使用 Trinity-RFT 而不打算修改代码：
 
 ```bash
-pip install trinity-rft==0.3.0
+pip install trinity-rft==0.3.1
 pip install flash-attn==2.8.1
 ```
 
 或使用 `uv`：
 
 ```bash
-uv pip install trinity-rft==0.3.0
+uv pip install trinity-rft==0.3.1
 uv pip install flash-attn==2.8.1
 ```
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name =  "trinity-rft"
-version = "0.3.1.dev0"
+version = "0.3.1"
 authors = [
     {name="Trinity-RFT Team", email="[email protected]"},
 ]
@@ -53,7 +53,7 @@ data = [
     "py-data-juicer>=1.4.3"
 ]
 agent = [
-    "agentscope"
+    "agentscope>=1.0.5"
 ]
 rm_gallery = [
     "rm-gallery>=0.1.5"

trinity/__init__.py

@@ -1,4 +1,4 @@
 # -*- coding: utf-8 -*-
 """Trinity-RFT (Reinforcement Fine-Tuning)"""
 
-__version__ = "0.3.1.dev0"
+__version__ = "0.3.1"