docs(links): fix broken documentation links

docs/en/examples/deepeyes.md

@@ -13,7 +13,7 @@ The DeepEyes example demonstrates **agentic multi-turn vision-language RL** usin
 
 ## Data Preparation
 
-DeepEyes uses the [Visual-Agent/DeepEyes](https://huggingface.co/datasets/Visual-Agent/DeepEyes) dataset from HuggingFace. The dataset contains visual QA samples with images stored inline (HF Image format).
+DeepEyes uses the [ChenShawn/DeepEyes-Datasets-47k](https://huggingface.co/datasets/ChenShawn/DeepEyes-Datasets-47k) dataset from HuggingFace. The dataset contains visual QA samples with images stored inline (HF Image format).
 
 ### Download the Dataset
 

docs/en/examples/low-precision-training.md

@@ -112,9 +112,11 @@ Output:
 For the FP8 training workflow you usually do **not** need this script — bridge mode (`--megatron-to-hf-mode bridge`) reads the FP8 HF directly. This tool is for offline conversion when you need a BF16 HF as input to a different pipeline (e.g. as a `--ref-load` source for another recipe, or to feed `convert_hf_to_int4.py`).
 :::
 
+<a id="convert-hf-to-int4"></a>
+
 ### `convert_hf_to_int4.py`
 
-Quantize a BF16 HF checkpoint to W4A16 (compressed-tensors). Uses the `fake_int4_quant_cuda` kernel, which must be built first (see [Build the int4_qat kernel](#build-the-int4_qat-kernel)).
+Quantize a BF16 HF checkpoint to W4A16 (compressed-tensors). Uses the `fake_int4_quant_cuda` kernel, which must be built first (see [Build the int4_qat kernel](#build-int4-qat-kernel)).
 
 ```bash
 python scripts/tools/convert_hf_to_int4.py \
@@ -185,7 +187,7 @@ Relax ships two reference recipes for Qwen3-30B-A3B (8-GPU colocate): **FP8 nati
 1. A BF16 HF checkpoint (e.g. `Qwen3-30B-A3B`).
 2. The Megatron patch at `docker/patch/megatron/20260506-85bced0ae.patch` applied (baked into the project Dockerfile). It provides both the FP8 overrides and the INT4 `_FakeInt4QuantizationSTE` that overrides `TEGroupedLinear._get_weight_tensors()`.
 
-The FP8 recipe additionally needs a TransformerEngine build with FP8 blockwise scaling support. The INT4 recipe additionally needs the `fake_int4_quant_cuda` CUDA extension built — see [Build the int4_qat kernel](#build-the-int4-qat-kernel) below.
+The FP8 recipe additionally needs a TransformerEngine build with FP8 blockwise scaling support. The INT4 recipe additionally needs the `fake_int4_quant_cuda` CUDA extension built — see [Build the int4_qat kernel](#build-int4-qat-kernel) below.
 
 ### FP8 Recipe
 
@@ -217,6 +219,8 @@ The FP8 recipe additionally needs a TransformerEngine build with FP8 blockwise s
 
 ### INT4 fake-QAT Recipe
 
+<a id="build-int4-qat-kernel"></a>
+
 #### Build the int4_qat kernel
 
 ```bash
@@ -331,5 +335,5 @@ Do not swap `--sglang-hf-checkpoint` to the BF16 cast for "consistency". SGLang'
 :::
 
 ::: tip
-This recipe assumes the W4A16 release was produced with **symmetric** quantization (matching the training-side STE). If you regenerate the W4A16 from BF16 via `convert_hf_to_int4.py`, always pass `--is-symmetric` — see the warning in the [Offline Quantization Tools](#convert_hf_to_int4-py) section above.
+This recipe assumes the W4A16 release was produced with **symmetric** quantization (matching the training-side STE). If you regenerate the W4A16 from BF16 via `convert_hf_to_int4.py`, always pass `--is-symmetric` — see the warning in the [Offline Quantization Tools](#convert-hf-to-int4) section above.
 :::

docs/en/guide/autoscaler-k8s-keda.md

@@ -450,7 +450,7 @@ ______________________________________________________________________
 
 - 引擎会使用初始模型权重运行
 - 后续权重更新在 Actor 的 `update_weights_fully_async()` 完成后自动触发
-- 如果对权重一致性要求严格，参考 [弹性 Rollout 扩缩容文档](../zh/guide/elastic-rollout.md) 中的权重同步机制
+- 如果对权重一致性要求严格，参考 [弹性 Rollout 扩缩容文档](./elastic-rollout.md) 中的权重同步机制
 
 ______________________________________________________________________
 
@@ -549,7 +549,7 @@ ______________________________________________________________________
 
 ## 延伸阅读
 
-- [弹性 Rollout 扩缩容](../zh/guide/elastic-rollout.md) — Relax 弹性扩缩容完整文档
-- [全异步训练流水线](../zh/guide/fully-async-training.md) — 弹性扩缩容的基础运行模式
+- [弹性 Rollout 扩缩容](./elastic-rollout.md) — Relax 弹性扩缩容完整文档
+- [全异步训练流水线](./fully-async-training.md) — 弹性扩缩容的基础运行模式
 - [KEDA 官方文档](https://keda.sh/docs/) — KEDA ScaledObject 配置参考
 - [Prometheus Operator](https://prometheus-operator.dev/) — ServiceMonitor 配置参考

docs/en/guide/customize-training.md

@@ -18,7 +18,7 @@ hf download Qwen/Qwen3-VL-4B-Instruct --local-dir /root/Qwen3-VL-4B-Instruct
 ### Megatron Weights to HF Weights
 
 ::: tip No Manual Conversion Needed with Megatron Bridge
-Relax uses [Megatron Bridge](../../../relax/backends/megatron/mbridge/) as the weight bridging layer for its training backend, automatically handling bidirectional HF ↔ Megatron weight conversion during training — **no manual conversion steps required**. Simply specify the following option in your launch script:
+Relax uses [Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) as the weight bridging layer for its training backend, automatically handling bidirectional HF ↔ Megatron weight conversion during training — **no manual conversion steps required**. Simply specify the following option in your launch script:
 :::
 
 ```bash

docs/zh/examples/deepeyes.md

@@ -13,7 +13,7 @@ DeepEyes 示例演示了使用 Relax 进行 **Agent 式多轮视觉语言强化
 
 ## 数据准备
 
-DeepEyes 使用 HuggingFace 上的 [Visual-Agent/DeepEyes](https://huggingface.co/datasets/Visual-Agent/DeepEyes) 数据集。数据集包含视觉问答样本，图像以 HF Image 格式内嵌存储。
+DeepEyes 使用 HuggingFace 上的 [ChenShawn/DeepEyes-Datasets-47k](https://huggingface.co/datasets/ChenShawn/DeepEyes-Datasets-47k) 数据集。数据集包含视觉问答样本，图像以 HF Image 格式内嵌存储。
 
 ### 下载数据集
 

docs/zh/examples/low-precision-training.md

@@ -112,9 +112,11 @@ python scripts/tools/convert_fp8_to_bf16.py \
 FP8 训练工作流下通常 **不需要** 这个脚本 — bridge 模式（`--megatron-to-hf-mode bridge`）会直接读取 FP8 HF。此工具用于离线转换：当你需要把 FP8 checkpoint 转回 BF16 HF 作为其他流水线的输入时（例如作为另一份配方的 `--ref-load`，或喂给 `convert_hf_to_int4.py`）。
 :::
 
+<a id="convert-hf-to-int4"></a>
+
 ### `convert_hf_to_int4.py`
 
-把 BF16 的 HF checkpoint 量化为 W4A16（compressed-tensors）。依赖 `fake_int4_quant_cuda` kernel，需先编译（见 [编译 int4_qat kernel](#编译-int4-qat-kernel)）。
+把 BF16 的 HF checkpoint 量化为 W4A16（compressed-tensors）。依赖 `fake_int4_quant_cuda` kernel，需先编译（见 [编译 int4_qat kernel](#build-int4-qat-kernel)）。
 
 ```bash
 python scripts/tools/convert_hf_to_int4.py \
@@ -185,7 +187,7 @@ Relax 在 Qwen3-30B-A3B（8 卡 colocate）上提供两条参考配方：**FP8 
 1. 一个 BF16 HF checkpoint（例如 `Qwen3-30B-A3B`）。
 2. 应用 Megatron patch `docker/patch/megatron/20260506-85bced0ae.patch`（项目 Dockerfile 已自动应用）—— 该 patch 同时提供 FP8 配套的 override 与 INT4 假量化的 `_FakeInt4QuantizationSTE`（override 了 `TEGroupedLinear._get_weight_tensors()`）。
 
-FP8 配方额外需要一个支持 FP8 blockwise scaling 的 TransformerEngine 构建；INT4 配方额外需要编译 `fake_int4_quant_cuda` CUDA 扩展，见下文 [编译 int4_qat kernel](#编译-int4-qat-kernel)。
+FP8 配方额外需要一个支持 FP8 blockwise scaling 的 TransformerEngine 构建；INT4 配方额外需要编译 `fake_int4_quant_cuda` CUDA 扩展，见下文 [编译 int4_qat kernel](#build-int4-qat-kernel)。
 
 ### FP8 低精度训练
 
@@ -216,6 +218,8 @@ FP8 配方额外需要一个支持 FP8 blockwise scaling 的 TransformerEngine 
 
 ### INT4 低精度训练
 
+<a id="build-int4-qat-kernel"></a>
+
 #### 编译 int4_qat kernel
 
 ```bash
@@ -329,5 +333,5 @@ bash scripts/entrypoint/ray-job.sh scripts/training/multimodal/run-kimi-k2.6-256
 :::
 
 ::: tip
-这个配方假设 W4A16 发布版是用**对称量化**生成的（与训练侧 STE 对齐）。如果你从 BF16 出发用 `convert_hf_to_int4.py` 重新生成 W4A16，必须带上 `--is-symmetric` —— 详见上文 [离线量化工具](#convert_hf_to_int4-py) 的 warning。
+这个配方假设 W4A16 发布版是用**对称量化**生成的（与训练侧 STE 对齐）。如果你从 BF16 出发用 `convert_hf_to_int4.py` 重新生成 W4A16，必须带上 `--is-symmetric` —— 详见上文 [离线量化工具](#convert-hf-to-int4) 的 warning。
 :::

docs/zh/guide/autoscaler-k8s-keda.md

@@ -450,7 +450,7 @@ ______________________________________________________________________
 
 - 引擎会使用初始模型权重运行
 - 后续权重更新在 Actor 的 `update_weights_fully_async()` 完成后自动触发
-- 如果对权重一致性要求严格，参考 [弹性 Rollout 扩缩容文档](../zh/guide/elastic-rollout.md) 中的权重同步机制
+- 如果对权重一致性要求严格，参考 [弹性 Rollout 扩缩容文档](./elastic-rollout.md) 中的权重同步机制
 
 ______________________________________________________________________
 
@@ -549,7 +549,7 @@ ______________________________________________________________________
 
 ## 延伸阅读
 
-- [弹性 Rollout 扩缩容](../zh/guide/elastic-rollout.md) — Relax 弹性扩缩容完整文档
-- [全异步训练流水线](../zh/guide/fully-async-training.md) — 弹性扩缩容的基础运行模式
+- [弹性 Rollout 扩缩容](./elastic-rollout.md) — Relax 弹性扩缩容完整文档
+- [全异步训练流水线](./fully-async-training.md) — 弹性扩缩容的基础运行模式
 - [KEDA 官方文档](https://keda.sh/docs/) — KEDA ScaledObject 配置参考
 - [Prometheus Operator](https://prometheus-operator.dev/) — ServiceMonitor 配置参考

docs/zh/guide/customize-training.md

@@ -18,7 +18,7 @@ hf download Qwen/Qwen3-VL-4B-Instruct --local-dir /root/Qwen3-VL-4B-Instruct
 ### Megatron 权重转 HF 权重
 
 ::: tip 使用 Megatron Bridge 无需手动转换
-Relax 默认使用 [Megatron Bridge](../../../relax/backends/megatron/mbridge/) 作为训练后端的权重桥接层，在训练过程中自动完成 HF ↔ Megatron 权重的双向转换，**无需任何手动转换步骤**。只需在启动脚本中指定以下选项即可：
+Relax 默认使用 [Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) 作为训练后端的权重桥接层，在训练过程中自动完成 HF ↔ Megatron 权重的双向转换，**无需任何手动转换步骤**。只需在启动脚本中指定以下选项即可：
 :::
 
 ```bash

examples/on_policy_distillation/README.md

@@ -66,9 +66,9 @@ SGLang per-position top-K log-prob transfer optimization (Qwen3-4B, simulated da
 
 ### Text OPD
 
-| Script                                                                                     | Description                                                                                 |
-| ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------- |
-| [`math_opd/run-opd-sampled-8xgpu-colocate.sh`](math_opd/run-opd-sampled-8xgpu-colocate.sh) | Math OPD, Qwen3-4B → Qwen3-4B-Non-Thinking-RL, `student_sampled` + adv mode, 8 GPU colocate |
+| Script                                                                                                   | Description                                                                                                                         |
+| -------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
+| [`math_opd/run-opd-qwen35-35B-A3B-8xgpu-colocate.sh`](math_opd/run-opd-qwen35-35B-A3B-8xgpu-colocate.sh) | Math OPD, student: Qwen3.5-35B-A3B; teacher: Qwen3.5-35B-A3B-GRPO-dapomath17k-400step; `student_sampled` + adv mode, 8 GPU colocate |
 
 ### Multimodal OPD
 
@@ -80,7 +80,7 @@ SGLang per-position top-K log-prob transfer optimization (Qwen3-4B, simulated da
 
 ## Common Combinations
 
-Based on [`math_opd/run-opd-sampled-8xgpu-colocate.sh`](math_opd/run-opd-sampled-8xgpu-colocate.sh), modify key variables to switch modes:
+Based on [`math_opd/run-opd-qwen35-35B-A3B-8xgpu-colocate.sh`](math_opd/run-opd-qwen35-35B-A3B-8xgpu-colocate.sh), modify key variables to switch modes:
 
 > **KL type note**: `student_sampled` supports only `reverse_kl` and `low_var_kl`; `student_topk`, `teacher_topk`, and `union` support `reverse_kl`, `forward_kl`, and `jsd` (set via `--opd-kl-type`; `jsd` can be tuned with `--opd-jsd-alpha`).
 

examples/on_policy_distillation/mopd/README.md

@@ -148,4 +148,4 @@ rollout 阶段按每条样本的 `data_source` 找到对应教师，请求 token
 ## 参考
 
 - [On-Policy Distillation - Relax Docs](../README.md)
-- [OPD 参数说明](../README.md#关键参数说明)
+- [OPD 参数配置示例](../README.md#common-combinations)