Add log manager to track experiement logs (#507)

Author: pan-x-c

README.md

@@ -32,20 +32,21 @@ Trinity-RFT provides functionalities for users with different backgrounds and ob
 
 ## 🚀 News
 
+* [2026-02] [[Release Notes]](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.5.1) Trinity-RFT v0.5.1 released: Enhanced VLM support, logging improvements, bug fixes.
 * [2026-02] [[Release Notes]](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.5.0) Trinity-RFT v0.5.0 released: colocate mode for single-GPU scenarios, trainer driven weight synchronization, automatic parallelism setting suggestion, and more.
 * [2026-01] 🎉 Three papers accepted by ICLR 2026: [CHORD](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/mix_chord), [BOTS](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/bots), and [Group-relative REINFORCE variants](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/rec_gsm8k). Try out these new algorithms in Trinity-RFT!
 * [2026-01] [[Release Notes]](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.4.1) Trinity-RFT v0.4.1 released: upgraded verl to v0.7.0, Tinker backend supports OpenAI API, bug fixes.
 * [2026-01] Introducing [R3L](https://github.com/shiweijiezero/R3L): a systematic reflect-then-retry RL mechanism with efficient language-guided exploration and stable off-policy learning ([paper](https://arxiv.org/abs/2601.03715)).
 * [2025-12] [[Release Notes]](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.4.0) Trinity-RFT v0.4.0 released: added [Tinker](https://thinkingmachines.ai/tinker/) backend for users **without GPUs**, add more benchmarks, enhance online RL and more.
 * [2025-12] Trinity-RFT powers the medical and health business of "Taobao Shangou", enabling the AI agent to understand vague symptoms, proactively ask follow-up questions, and provide precise recommendations ([News](https://tech.china.com.cn/sx/20251201/411376.shtml)).
-* [2025-11] [[Release Notes](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.3.3)] Trinity-RFT v0.3.3 released: bug fixes.
 * [2025-11] Introducing [Learn-to-Ask](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/learn_to_ask): a framework for training proactive dialogue agents from offline expert data  ([paper](https://arxiv.org/pdf/2510.25441)).
 * [2025-11] Introducing [BOTS](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/bots): online RL task selection for efficient LLM fine-tuning ([paper](https://arxiv.org/pdf/2510.26374)).
 * [2025-09] [Our paper](https://arxiv.org/pdf/2509.24203) reveals a novel off-policy interpretation for group-relative REINFORCE and its variants like GRPO and AsymRE ([implementation](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/rec_gsm8k)).
 * [2025-08] Introducing [CHORD](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/mix_chord): dynamic SFT + RL integration for advanced LLM fine-tuning ([paper](https://arxiv.org/pdf/2508.11408)).
 
 <details><summary> More... </summary>
 <ul>
+  <li> [2025-11] Trinity-RFT v0.3.3 released: bug fixes.</li>
   <li> [2025-11] Trinity-RFT v0.3.2 released: bug fixes and advanced task selection & scheduling.</li>
   <li> [2025-10] Trinity-RFT v0.3.1 released: multi-stage training support, improved agentic RL examples, LoRA support, debug mode and new RL algorithms.</li>
   <li> [2025-09] Trinity-RFT v0.3.0 released: enhanced Buffer, FSDP2 & Megatron support, multi-modal models, and new RL algorithms/examples.</li>
@@ -67,7 +68,7 @@ Trinity-RFT provides functionalities for users with different backgrounds and ob
 | *Full-lifecycle data pipelines*   | • [Rollout task mixing and selection](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/develop_selector.html)<br>• [Online task curriculum](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/bots) (📝 [paper](https://arxiv.org/pdf/2510.26374))<br>• [Research project: learn-to-ask](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/learn_to_ask) (📝 [paper](https://arxiv.org/pdf/2510.25441))<br>• [Experience replay with prioritization](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/ppo_countdown_exp_replay)<br>• [Advanced data processing & human-in-the-loop](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/example_data_functionalities.html) |
 | *Algorithm development*           | • [RL algorithm development with Trinity-RFT](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/example_mix_algo.html) (📝 [paper](https://arxiv.org/pdf/2508.11408))<br>• [Research project: R3L (reflect-then-retry RL)](https://github.com/shiweijiezero/R3L) (📝 [paper](https://arxiv.org/abs/2601.03715))<br>• [Research project: group-relative REINFORCE](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/rec_gsm8k) (📝 [paper](https://arxiv.org/abs/2509.24203))<br>• Non-verifiable domains: [RULER](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler), [trainable RULER](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler), [rubric-as-reward](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_rubric_as_reward) |
 | *Benchmarks*                      | • [Benchmark toolkit (quick verification & experimentation)](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/README.md)<br>• [Guru-Math benchmark & comparison with veRL](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/reports/guru_math.md)<br>• [FrozenLake benchmark & comparison with rLLM](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/reports/frozenlake.md)<br>• [Alfworld benchmark & comparison with rLLM](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/reports/alfworld.md) |
-| *Going deeper into Trinity-RFT*   | • [Full configurations](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/trinity_configs.html)<br>• [GPU resource and training configuration guide](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/trinity_gpu_configs.html)<br>• [Understand the coordination between explorer and trainer](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/synchronizer.html)<br>• [How to align configuration with veRL](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/align_with_verl.html) |
+| *Going deeper into Trinity-RFT*   | • [Full configurations](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/trinity_configs.html)<br>• [GPU resource and training configuration guide](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/trinity_gpu_configs.html)<br>• [Training VLM](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_vlm)<br>• [Understand the coordination between explorer and trainer](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/synchronizer.html)<br>• [How to align configuration with veRL](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/align_with_verl.html) |
 
 > [!NOTE]
 > For more tutorials, please refer to the [Trinity-RFT documentation](https://agentscope-ai.github.io/Trinity-RFT/).

README_zh.md

@@ -41,6 +41,7 @@ Trinity-RFT 面向不同背景和目标的用户提供相应功能：
 
 ## 🚀 新闻
 
+* [2026-02] [[发布说明]](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.5.1) Trinity-RFT v0.5.1 发布：增强 VLM 支持，改进日志系统，修复若干 Bug。
 * [2026-02] [[发布说明]](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.5.0) Trinity-RFT v0.5.0 发布：单 GPU 场景下的 colocate 模式，trainer 驱动的权重同步，自动并行设置建议等新功能。
 * [2026-01] 🎉 三篇论文被 ICLR 2026 接收：[CHORD](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/mix_chord)、[BOTS](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/bots) 和 [Group-relative REINFORCE 系列变种](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/rec_gsm8k)。在 Trinity-RFT 中尝试这些新算法吧！
 * [2026-01] [[发布说明]](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.4.1) Trinity-RFT v0.4.1 发布：升级 verl 至 v0.7.0，Tinker 后端支持 OpenAI API，修复若干 Bug。
@@ -80,7 +81,7 @@ Trinity-RFT 面向不同背景和目标的用户提供相应功能：
 | *全生命周期的数据流水线* | + [Rollout 任务混合与选取](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/develop_selector.html)<br>+ [在线任务选择](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/bots) (📝 [论文](https://arxiv.org/pdf/2510.26374))<br>+ [研究项目：learn-to-ask](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/learn_to_ask) (📝 [论文](https://arxiv.org/pdf/2510.25441)) <br>+ [经验回放机制](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/ppo_countdown_exp_replay)<br>+ [高级数据处理能力 &  Human-in-the-loop](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/example_data_functionalities.html)  |
 | *强化学习算法开发* | + [使用 Trinity-RFT 进行 RL 算法开发](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/example_mix_algo.html) (📝 [论文](https://arxiv.org/pdf/2508.11408))<br>+ [研究项目: R3L (基于反思-重试的强化学习)](https://github.com/shiweijiezero/R3L) (📝 [论文](https://arxiv.org/abs/2601.03715))<br>+ [研究项目: group-relative REINFORCE](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/rec_gsm8k) (📝 [论文](https://arxiv.org/abs/2509.24203)) <br>+ 不可验证的领域: [RULER](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler), [可训练 RULER](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler), [rubric-as-reward](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_rubric_as_reward) |
 | *基准测试* | + [基准测试工具 (快速验证与实验)](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/README.md)<br>+ [Guru-Math 测试 & 对比 veRL](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/reports/guru_math.md)<br>+ [FrozenLake 测试 & 对比 rLLM](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/reports/frozenlake.md)<br>+ [Alfworld 测试 & 对比 rLLM](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/reports/alfworld.md) |
-| *深入认识 Trinity-RFT* | + [完整配置指南](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/trinity_configs.html)<br>+ [GPU 资源与训练配置对应指南](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/trinity_gpu_configs.html)<br>+ [理解 explorer-trainer 同步逻辑](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/synchronizer.html)<br>+ [如何与 verl 对齐配置](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/align_with_verl.html)   |
+| *深入了解 Trinity-RFT* | + [完整配置指南](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/trinity_configs.html)<br>+ [GPU 资源与训练配置对应指南](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/trinity_gpu_configs.html)<br>+ [训练多模态模型](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_vlm)<br>+ [理解 explorer-trainer 同步逻辑](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/synchronizer.html)<br>+ [如何与 verl 对齐配置](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/align_with_verl.html)   |
 
 
 > [!NOTE]

docs/sphinx_doc/source/main.md

@@ -32,7 +32,7 @@ Trinity-RFT provides functionalities for users with different backgrounds and ob
 | *Full-lifecycle data pipelines* | + [Rollout task mixing and selection](/tutorial/develop_selector.md)<br>+ [Online task curriculum](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/bots) (📝 [paper](https://arxiv.org/pdf/2510.26374))<br>+ [Research project: learn-to-ask](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/learn_to_ask) (📝 [paper](https://arxiv.org/pdf/2510.25441)) <br>+ [Experience replay with prioritization](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/ppo_countdown_exp_replay)<br>+ [Advanced data processing & human-in-the-loop](/tutorial/example_data_functionalities.md)  |
 | *Algorithm development* | + [RL algorithm development with Trinity-RFT](/tutorial/example_mix_algo.md) (📝 [paper](https://arxiv.org/pdf/2508.11408))<br>+ [Research project: R3L (reflect-then-retry RL)](https://github.com/shiweijiezero/R3L) (📝 [paper](https://arxiv.org/abs/2601.03715))<br>+ [Research project: group-relative REINFORCE](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/rec_gsm8k) (📝 [paper](https://arxiv.org/abs/2509.24203)) <br>+ Non-verifiable domains: [RULER](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler), [trainable RULER](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler), [rubric-as-reward](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_rubric_as_reward) |
 | *Benchmarks* | + [Benchmark toolkit (quick verification & experimentation)](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/README.md)<br>+ [Guru-Math benchmark & comparison with veRL](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/reports/guru_math.md)<br>+ [FrozenLake benchmark & comparison with rLLM](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/reports/frozenlake.md)<br>+ [Alfworld benchmark & comparison with rLLM](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/reports/alfworld.md) |
-| *Going deeper into Trinity-RFT* | + [Full configurations](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/trinity_configs.html)<br>+ [GPU resource and training configuration guide](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/trinity_gpu_configs.html)<br>+ [Understand the coordination between explorer and trainer](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/synchronizer.html)<br>+ [How to align configuration with veRL](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/align_with_verl.html)    |
+| *Going deeper into Trinity-RFT*   | + [Full configurations](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/trinity_configs.html)<br>+ [GPU resource and training configuration guide](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/trinity_gpu_configs.html)<br>+ [Training VLM](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_vlm)<br>+ [Understand the coordination between explorer and trainer](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/synchronizer.html)<br>+ [How to align configuration with veRL](https://agentscope-ai.github.io/Trinity-RFT/en/main/tutorial/align_with_verl.html) |
 
 
 

docs/sphinx_doc/source/tutorial/develop_workflow.md

@@ -562,4 +562,4 @@ Each runner will log its output to a separate log file. The log file naming conv
     └── ...
 ```
 
-If you found errors or blocking issues during training, you can check the corresponding log files for detailed information to help diagnose and fix problems.
+Trinity-RFT also provide a convenient command `log` to view these logs in real-time. You can use `trinity log --log-dir /path/to/log/dir -k explorer_runner` command to filter and view the logs of all runners at once or use `trinity log --log-dir /path/to/log/dir -k explorer_runner_0` to view the logs of a specific runner. If you encounter errors or blocking issues during training, you can check the corresponding log files for detailed information to help diagnose and resolve the problems.

docs/sphinx_doc/source_zh/main.md

@@ -31,8 +31,7 @@ Trinity-RFT 面向不同背景和目标的用户提供相应功能：
 | *全生命周期的数据流水线* | + [Rollout 任务混合与选取](/tutorial/develop_selector.md)<br>+ [在线任务选择](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/bots) (📝 [论文](https://arxiv.org/pdf/2510.26374))<br>+ [研究项目：learn-to-ask](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/learn_to_ask) (📝 [论文](https://arxiv.org/pdf/2510.25441)) <br>+ [经验回放机制](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/ppo_countdown_exp_replay)<br>+ [高级数据处理能力 &  Human-in-the-loop](/tutorial/example_data_functionalities.md)  |
 | *强化学习算法开发* | + [使用 Trinity-RFT 进行 RL 算法开发](/tutorial/example_mix_algo.md) (📝 [论文](https://arxiv.org/pdf/2508.11408))<br>+ [研究项目: R3L (基于反思-重试的强化学习)](https://github.com/shiweijiezero/R3L) (📝 [论文](https://arxiv.org/abs/2601.03715))<br>+ [研究项目: group-relative REINFORCE](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/rec_gsm8k) (📝 [论文](https://arxiv.org/abs/2509.24203)) <br>+ 不可验证的领域: [RULER](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler), [可训练 RULER](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler), [rubric-as-reward](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_rubric_as_reward) |
 | *基准测试* | + [基准测试工具 (快速验证与实验)](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/README.md)<br>+ [Guru-Math 测试 & 对比 veRL](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/reports/guru_math.md)<br>+ [FrozenLake 测试 & 对比 rLLM](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/reports/frozenlake.md)<br>+ [Alfworld 测试 & 对比 rLLM](https://github.com/agentscope-ai/Trinity-RFT/tree/main/benchmark/reports/alfworld.md) |
-| *深入认识 Trinity-RFT* | + [完整配置指南](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/trinity_configs.html)<br>+ [GPU 资源与训练配置对应指南](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/trinity_gpu_configs.html)<br>+ [理解 explorer-trainer 同步逻辑](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/synchronizer.html)<br>+ [如何与 verl 对齐配置](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/align_with_verl.html)   |
-
+| *深入了解 Trinity-RFT* | + [完整配置指南](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/trinity_configs.html)<br>+ [GPU 资源与训练配置对应指南](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/trinity_gpu_configs.html)<br>+ [训练多模态模型](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/grpo_vlm)<br>+ [理解 explorer-trainer 同步逻辑](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/synchronizer.html)<br>+ [如何与 verl 对齐配置](https://agentscope-ai.github.io/Trinity-RFT/zh/main/tutorial/align_with_verl.html)   |
 
 ## 🌟 核心特性
 

docs/sphinx_doc/source_zh/tutorial/develop_workflow.md

@@ -556,4 +556,4 @@ class ExampleWorkflow(Workflow):
     └── ...
 ```
 
-如果训练过程中出现报错或阻塞等异常情况，可通过查看对应日志文件快速定位问题，从而高效调试和优化工作流实现。
+Trinity-RFT 还提供了一个方便的 `log` 命令来实时查看这些日志。你可以使用 `trinity log --log-dir /path/to/log/dir -k explorer_runner` 命令来过滤并查看所有 workflow runner 的日志，或者使用 `trinity log --log-dir /path/to/log/dir -k explorer_runner_0` 来查看特定 workflow runner 的日志。