Add R3L to readme and doc

README.md

@@ -32,6 +32,7 @@ Trinity-RFT provides functionalities for users with different backgrounds and ob
 
 ## 🚀 News
 
+* [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/modelscope/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/modelscope/Trinity-RFT/releases/tag/v0.3.3)] Trinity-RFT v0.3.3 released: bug fixes.
@@ -61,7 +62,7 @@ Trinity-RFT provides functionalities for users with different backgrounds and ob
 | *Run diverse RFT modes*           | • [Quick start: GRPO on GSM8k](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_reasoning_basic.html)<br>• [Off-policy RFT](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_reasoning_advanced.html)<br>• [Fully asynchronous RFT](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_async_mode.html)<br>• [Offline learning by DPO or SFT](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_dpo.html)<br>• [RFT without local GPU (Tinker Backend)](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_tinker_backend.html) |
 | *Multi-step agentic RL*           | • [Concatenated multi-turn workflow](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_multi_turn.html)<br>• [General multi-step workflow](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_step_wise.html)<br>• [ReAct workflow with an agent framework](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_react.html)<br>• [Example: train a web-search agent](https://github.com/modelscope/Trinity-RFT/tree/main/examples/agentscope_websearch) |
 | *Full-lifecycle data pipelines*   | • [Rollout task mixing and selection](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/develop_selector.html)<br>• [Online task curriculum](https://github.com/modelscope/Trinity-RFT/tree/main/examples/bots) (📝 [paper](https://arxiv.org/pdf/2510.26374))<br>• [Research project: learn-to-ask](https://github.com/modelscope/Trinity-RFT/tree/main/examples/learn_to_ask) (📝 [paper](https://arxiv.org/pdf/2510.25441))<br>• [Experience replay with prioritization](https://github.com/modelscope/Trinity-RFT/tree/main/examples/ppo_countdown_exp_replay)<br>• [Advanced data processing & human-in-the-loop](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_data_functionalities.html) |
-| *Algorithm development*           | • [RL algorithm development with Trinity-RFT](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_mix_algo.html) (📝 [paper](https://arxiv.org/pdf/2508.11408))<br>• [Research project: group-relative REINFORCE](https://github.com/modelscope/Trinity-RFT/tree/main/examples/rec_gsm8k) (📝 [paper](https://arxiv.org/abs/2509.24203))<br>• Non-verifiable domains: [RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler), [trainable RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler), [rubric-as-reward](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_rubric_as_reward) |
+| *Algorithm development*           | • [RL algorithm development with Trinity-RFT](https://modelscope.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/modelscope/Trinity-RFT/tree/main/examples/rec_gsm8k) (📝 [paper](https://arxiv.org/abs/2509.24203))<br>• Non-verifiable domains: [RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler), [trainable RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler), [rubric-as-reward](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_rubric_as_reward) |
 | *Benchmarks*                      | • [Benchmark toolkit (quick verification & experimentation)](https://github.com/modelscope/Trinity-RFT/tree/main/benchmark/README.md)<br>• [Guru-Math benchmark & comparison with veRL](https://github.com/modelscope/Trinity-RFT/tree/main/benchmark/reports/guru_math.md)<br>• [FrozenLake benchmark & comparison with rLLM](https://github.com/modelscope/Trinity-RFT/tree/main/benchmark/reports/frozenlake.md)<br>• [Alfworld benchmark & comparison with rLLM](https://github.com/modelscope/Trinity-RFT/tree/main/benchmark/reports/alfworld.md) |
 | *Going deeper into Trinity-RFT*   | • [Full configurations](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/trinity_configs.html)<br>• [GPU resource and training configuration guide](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/trinity_gpu_configs.html)<br>• [Understand the coordination between explorer and trainer](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/synchronizer.html)<br>• [How to align configuration with veRL](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/align_with_verl.html) |
 

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
 
+* [2026-01] 推出 [R3L](https://github.com/shiweijiezero/R3L)：基于反思-重试的强化学习机制，由自然语言反馈引导高效探索，并达成稳定的 off-policy 学习（[论文](https://arxiv.org/abs/2601.03715)）。
 * [2025-12] [[发布说明]](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.4.0) Trinity-RFT v0.4.0 发布：新增[Tinker](https://thinkingmachines.ai/tinker/) 后端以支持在 **无 GPU** 的设备上训练，增加更多基准测试，增强在线 RL 等功能。
 * [2025-12] Trinity-RFT 已支持 [tinker](https://thinkingmachines.ai/tinker/) 训练后端，可在**无 GPU 的设备**上进行模型训练。
 * [2025-12] Trinity-RFT 助力淘宝闪购医药健康业务，让 AI 智能体能够理解模糊症状、主动询问后续问题，并提供精准推荐（[新闻](https://tech.china.com.cn/sx/20251201/411376.shtml)）。
@@ -74,7 +75,7 @@ Trinity-RFT 面向不同背景和目标的用户提供相应功能：
 | *运行各种 RFT 模式* | + [快速开始：在 GSM8k 上运行 GRPO](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_reasoning_basic.html)<br>+ [Off-policy RFT](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_reasoning_advanced.html)<br>+ [全异步 RFT](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_async_mode.html)<br>+ [通过 DPO 或 SFT 进行离线学习](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_dpo.html)<br>+ [在无GPU环境下运行RFT训练（Tinker 后端）](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_tinker_backend.html)   |
 | *多轮智能体强化学习* | + [拼接多轮任务](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_multi_turn.html)<br>+ [通用多轮任务](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_step_wise.html)<br>+ [调用智能体框架中的 ReAct 工作流](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_react.html)  <br>+ [例子：训练一个网络搜索智能体](https://github.com/modelscope/Trinity-RFT/tree/main/examples/agentscope_websearch) |
 | *全生命周期的数据流水线* | + [Rollout 任务混合与选取](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/develop_selector.html)<br>+ [在线任务选择](https://github.com/modelscope/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/modelscope/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/modelscope/Trinity-RFT/tree/main/examples/ppo_countdown_exp_replay)<br>+ [高级数据处理能力 &  Human-in-the-loop](https://modelscope.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://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_mix_algo.html) (📝 [论文](https://arxiv.org/pdf/2508.11408))<br>+ [研究项目: group-relative REINFORCE](https://github.com/modelscope/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/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler), [可训练 RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler), [rubric-as-reward](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_rubric_as_reward) |
+| *强化学习算法开发* | + [使用 Trinity-RFT 进行 RL 算法开发](https://modelscope.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/modelscope/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/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler), [可训练 RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler), [rubric-as-reward](https://github.com/modelscope/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/modelscope/Trinity-RFT/tree/main/benchmark/README.md)<br>+ [Guru-Math 测试 & 对比 veRL](https://github.com/modelscope/Trinity-RFT/tree/main/benchmark/reports/guru_math.md)<br>+ [FrozenLake 测试 & 对比 rLLM](https://github.com/modelscope/Trinity-RFT/tree/main/benchmark/reports/frozenlake.md)<br>+ [Alfworld 测试 & 对比 rLLM](https://github.com/modelscope/Trinity-RFT/tree/main/benchmark/reports/alfworld.md) |
 | *深入认识 Trinity-RFT* | + [完整配置指南](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/trinity_configs.html)<br>+ [GPU 资源与训练配置对应指南](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/trinity_gpu_configs.html)<br>+ [理解 explorer-trainer 同步逻辑](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/synchronizer.html)<br>+ [如何与 verl 对齐配置](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/align_with_verl.html)   |
 

docs/sphinx_doc/source/main.md

@@ -30,7 +30,7 @@ Trinity-RFT provides functionalities for users with different backgrounds and ob
 | *Run diverse RFT modes* | + [Quick start: GRPO on GSM8k](/tutorial/example_reasoning_basic.md)<br>+ [Off-policy RFT](/tutorial/example_reasoning_advanced.md)<br>+ [Fully asynchronous RFT](/tutorial/example_async_mode.md)<br>+ [Offline learning by DPO or SFT](/tutorial/example_dpo.md)     |
 | *Multi-step agentic RL* | + [Concatenated multi-turn workflow](/tutorial/example_multi_turn.md)<br>+ [General multi-step workflow](/tutorial/example_step_wise.md)<br>+ [ReAct workflow with an agent framework](/tutorial/example_react.md)  <br>+ [Example: train a web-search agent](https://github.com/modelscope/Trinity-RFT/tree/main/examples/agentscope_websearch) |
 | *Full-lifecycle data pipelines* | + [Rollout task mixing and selection](/tutorial/develop_selector.md)<br>+ [Online task curriculum](https://github.com/modelscope/Trinity-RFT/tree/main/examples/bots) (📝 [paper](https://arxiv.org/pdf/2510.26374))<br>+ [Research project: learn-to-ask](https://github.com/modelscope/Trinity-RFT/tree/main/examples/learn_to_ask) (📝 [paper](https://arxiv.org/pdf/2510.25441)) <br>+ [Experience replay with prioritization](https://github.com/modelscope/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: group-relative REINFORCE](https://github.com/modelscope/Trinity-RFT/tree/main/examples/rec_gsm8k) (📝 [paper](https://arxiv.org/abs/2509.24203)) <br>+ Non-verifiable domains: [RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler), [trainable RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler), [rubric-as-reward](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_rubric_as_reward) |
+| *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/modelscope/Trinity-RFT/tree/main/examples/rec_gsm8k) (📝 [paper](https://arxiv.org/abs/2509.24203)) <br>+ Non-verifiable domains: [RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler), [trainable RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler), [rubric-as-reward](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_rubric_as_reward) |
 | *Benchmarks* | + [Benchmark toolkit (quick verification & experimentation)](https://github.com/modelscope/Trinity-RFT/tree/main/benchmark/README.md)<br>+ [Guru-Math benchmark & comparison with veRL](https://github.com/modelscope/Trinity-RFT/tree/main/benchmark/reports/guru_math.md)<br>+ [FrozenLake benchmark & comparison with rLLM](https://github.com/modelscope/Trinity-RFT/tree/main/benchmark/reports/frozenlake.md)<br>+ [Alfworld benchmark & comparison with rLLM](https://github.com/modelscope/Trinity-RFT/tree/main/benchmark/reports/alfworld.md) |
 | *Going deeper into Trinity-RFT* | + [Full configurations](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/trinity_configs.html)<br>+ [GPU resource and training configuration guide](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/trinity_gpu_configs.html)<br>+ [Understand the coordination between explorer and trainer](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/synchronizer.html)<br>+ [How to align configuration with veRL](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/align_with_verl.html)    |
 

docs/sphinx_doc/source_zh/main.md

@@ -29,7 +29,7 @@ Trinity-RFT 面向不同背景和目标的用户提供相应功能：
 | *运行各种 RFT 模式* | + [快速开始：在 GSM8k 上运行 GRPO](/tutorial/example_reasoning_basic.md)<br>+ [Off-policy RFT](/tutorial/example_reasoning_advanced.md)<br>+ [全异步 RFT](/tutorial/example_async_mode.md)<br>+ [通过 DPO 或 SFT 进行离线学习](/tutorial/example_dpo.md)     |
 | *多轮智能体强化学习* | + [拼接多轮任务](/tutorial/example_multi_turn.md)<br>+ [通用多轮任务](/tutorial/example_step_wise.md)<br>+ [调用智能体框架中的 ReAct 工作流](/tutorial/example_react.md)  <br>+ [例子：训练一个网络搜索智能体](https://github.com/modelscope/Trinity-RFT/tree/main/examples/agentscope_websearch) |
 | *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/modelscope/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/modelscope/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/modelscope/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: group-relative REINFORCE](https://github.com/modelscope/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/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler), [可训练 RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler), [rubric-as-reward](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_rubric_as_reward) |
+| *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/modelscope/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/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler), [可训练 RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler), [rubric-as-reward](https://github.com/modelscope/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/modelscope/Trinity-RFT/tree/main/benchmark/README.md)<br>+ [Guru-Math 测试 & 对比 veRL](https://github.com/modelscope/Trinity-RFT/tree/main/benchmark/reports/guru_math.md)<br>+ [FrozenLake 测试 & 对比 rLLM](https://github.com/modelscope/Trinity-RFT/tree/main/benchmark/reports/frozenlake.md)<br>+ [Alfworld 测试 & 对比 rLLM](https://github.com/modelscope/Trinity-RFT/tree/main/benchmark/reports/alfworld.md) |
 | *深入认识 Trinity-RFT* | + [完整配置指南](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/trinity_configs.html)<br>+ [GPU 资源与训练配置对应指南](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/trinity_gpu_configs.html)<br>+ [理解 explorer-trainer 同步逻辑](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/synchronizer.html)<br>+ [如何与 verl 对齐配置](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/align_with_verl.html)   |