[script] provide on-policy distillation script & update GKD doc

docs/source/Instruction/GKD.md

@@ -33,22 +33,22 @@ $$
 #### Forward KL（前向 KL）
 
 $$
-\text{KL}(P_{\text{student}} \| P_{\text{teacher}}) = \sum_v P_{\text{student}}(v) \log \frac{P_{\text{student}}(v)}{P_{\text{teacher}}(v)}
+\text{KL}(P_{\text{teacher}} \| P_{\text{student}}) = \sum_v P_{\text{teacher}}(v) \log \frac{P_{\text{teacher}}(v)}{P_{\text{student}}(v)}
 $$
 
-**特性**：Mode-seeking（寻模）
-- 期望在学生分布下计算
-- 学生模型倾向于集中在教师模型的峰值区域（高概率区域）
+**特性**：Mode-covering
+- 期望在教师分布下计算
+- 学生模型倾向于覆盖教师的整个分布（包括低概率区域）
 
-#### Reverse KL（反向 KL）
 
+#### Reverse KL（反向 KL）
 $$
-\text{KL}(P_{\text{teacher}} \| P_{\text{student}}) = \sum_v P_{\text{teacher}}(v) \log \frac{P_{\text{teacher}}(v)}{P_{\text{student}}(v)}
+\text{KL}(P_{\text{student}} \| P_{\text{teacher}}) = \sum_v P_{\text{student}}(v) \log \frac{P_{\text{student}}(v)}{P_{\text{teacher}}(v)}
 $$
 
-**特性**：Mode-covering（覆模）
-- 期望在教师分布下计算
-- 学生模型倾向于覆盖教师的整个分布（包括低概率区域）
+**特性**：Mode-seeking
+- 期望在学生分布下计算
+- 学生模型倾向于集中在教师模型的峰值区域（高概率区域）
 
 ### 广义 Jensen-Shannon 散度（Generalized JSD）
 
@@ -78,8 +78,8 @@ $$
 其中 $M = \beta \cdot P_{\text{teacher}} + (1-\beta) \cdot P_{\text{student}}$
 
 > 对极端情况（$\beta = 0$ 或 $\beta = 1$），直接计算单个 KL 散度：
-> - 当 $\beta = 0$ 时：直接定义 $D = \text{KL}(P_{\text{teacher}} \| P_{\text{student}})$（Reverse KL，Mode-covering）
-> - 当 $\beta = 1$ 时：直接定义 $D = \text{KL}(P_{\text{student}} \| P_{\text{teacher}})$（Forward KL，Mode-seeking）
+> - 当 $\beta = 0$ 时：直接定义 $D = \text{KL}(P_{\text{teacher}} \| P_{\text{student}})$（Forward KL，Mode-covering）
+> - 当 $\beta = 1$ 时：直接定义 $D = \text{KL}(P_{\text{student}} \| P_{\text{teacher}})$（Reverse KL，Mode-seeking）
 > - 当 $0 < \beta < 1$ 时：使用上述混合分布公式进行插值
 
 通过调节 $\beta$ 参数，可以在不同的散度度量之间进行插值，当 $\beta = 0.5$ 时，散度为标准的对称 JSD。
@@ -142,8 +142,8 @@ loss = D_JSD(P_teacher(·|x,y), P_student(·|x,y))
 | 参数 | 类型 | 默认值 | 取值范围 | 说明 |
 |------|------|--------|---------|------|
 | `--teacher_model` | str | 必需 | - | 教师模型路径或模型 ID |
-| `--beta` | float | 0.5 | [0.0, 1.0] | 散度插值系数<br>• 0.0: Reverse KL (覆模，更多样)<br>• 0.5: JSD (平衡，**推荐**)<br>• 1.0: Forward KL (寻模，更专注) |
-| `--lmbda` | float | 0.5 | [0.0, 1.0] | On-Policy 学习触发概率<br>• 0.0: 纯 Off-Policy<br>• 0.5: 混合策略 (**推荐**)<br>• 1.0: 纯 On-Policy |
+| `--beta` | float | 0.5 | [0.0, 1.0] | 散度插值系数<br>• 0.0: Forward KL <br>• 0.5: JSD (平衡)<br>• 1.0: Reverse KL |
+| `--lmbda` | float | 0.5 | [0.0, 1.0] | On-Policy 学习触发概率<br>• 0.0: 纯 Off-Policy<br>• 0.5: 混合策略<br>• 1.0: 纯 On-Policy |
 | `--seq_kd` | bool | False | True/False | 是否使用教师生成序列<br>• False: 非 on-policy 时使用数据集<br>• True: 非 on-policy 时使用教师生成 |
 | `--temperature` | float | 0.9 | > 0 | 生成采样温度，控制随机性 |
 | `--max_completion_length` | int | 512 | > 0 | 生成时的最大 token 数 |
@@ -200,3 +200,13 @@ swift rlhf \
 ```
 
 训练脚本参考[这里](https://github.com/modelscope/ms-swift/tree/main/examples/train/multimodal/rlhf/gkd/fast.sh)
+
+## On-Policy Distillation
+
+我们可以通过设置以下参数实现 Thinking Machine Lab blog 中的[On-Policy Distillation](https://thinkingmachines.ai/blog/on-policy-distillation/)训练。
+```bash
+--lmbda 1 # on-policy
+--beta 1 # reverse
+```
+
+相关脚本可以参考[这里](https://github.com/modelscope/ms-swift/tree/main/examples/train/on_policy_distillation.sh)

docs/source/Instruction/GRPO/AdvancedResearch/index.rst

@@ -7,4 +7,5 @@ Advanced Research
    DAPO.md
    deepeyes.md
    GSPO.md
+   RLOO.md
    CHORD.md

docs/source_en/Instruction/GKD.md

@@ -33,22 +33,22 @@ In knowledge distillation, there are two choices depending on the order of the t
 #### Forward KL
 
 $$
-\text{KL}(P_{\text{student}} \| P_{\text{teacher}}) = \sum_v P_{\text{student}}(v) \log \frac{P_{\text{student}}(v)}{P_{\text{teacher}}(v)}
+\text{KL}(P_{\text{teacher}} \| P_{\text{student}}) = \sum_v P_{\text{teacher}}(v) \log \frac{P_{\text{teacher}}(v)}{P_{\text{student}}(v)}
 $$
 
-**Characteristics**: Mode-seeking
-- Expectation is computed under the student distribution
-- The student model tends to concentrate on the peak regions (high-probability areas) of the teacher model
+**Characteristics**: Mode-covering
+- Expectation is computed under the teacher distribution
+- The student model tends to cover the entire teacher distribution (including low-probability regions)
 
 #### Reverse KL
 
 $$
-\text{KL}(P_{\text{teacher}} \| P_{\text{student}}) = \sum_v P_{\text{teacher}}(v) \log \frac{P_{\text{teacher}}(v)}{P_{\text{student}}(v)}
+\text{KL}(P_{\text{student}} \| P_{\text{teacher}}) = \sum_v P_{\text{student}}(v) \log \frac{P_{\text{student}}(v)}{P_{\text{teacher}}(v)}
 $$
 
-**Characteristics**: Mode-covering
-- Expectation is computed under the teacher distribution
-- The student model tends to cover the entire teacher distribution (including low-probability regions)
+**Characteristics**: Mode-seeking
+- Expectation is computed under the student distribution
+- The student model tends to concentrate on the peak regions (high-probability areas) of the teacher model
 
 ### Generalized Jensen-Shannon Divergence (Generalized JSD)
 
@@ -78,8 +78,8 @@ $$
 Where $M = \beta \cdot P_{\text{teacher}} + (1-\beta) \cdot P_{\text{student}}$
 
 > For extreme cases ($\beta = 0$ or $\beta = 1$), directly compute a single KL divergence:
-> - When $\beta = 0$: directly define $D = \text{KL}(P_{\text{teacher}} \| P_{\text{student}})$ (Reverse KL, Mode-covering)
-> - When $\beta = 1$: directly define $D = \text{KL}(P_{\text{student}} \| P_{\text{teacher}})$ (Forward KL, Mode-seeking)
+> - When $\beta = 0$: directly define $D = \text{KL}(P_{\text{teacher}} \| P_{\text{student}})$ (Forward KL, Mode-covering)
+> - When $\beta = 1$: directly define $D = \text{KL}(P_{\text{student}} \| P_{\text{teacher}})$ (Reverse KL, Mode-seeking)
 > - When $0 < \beta < 1$: use the above mixture distribution formula for interpolation
 
 By adjusting the $\beta$ parameter, interpolation can be performed between different divergence metrics. When $\beta = 0.5$, the divergence is the standard symmetric JSD.
@@ -142,7 +142,7 @@ We can perform GKD training by setting the following parameters:
 | Parameter | Type | Default | Range | Description |
 |------|------|--------|---------|------|
 | `--teacher_model` | str | Required | - | Teacher model path or model ID |
-| `--beta` | float | 0.5 | [0.0, 1.0] | Divergence interpolation coefficient<br>• 0.0: Reverse KL (mode-covering, more diverse)<br>• 0.5: JSD (balanced, **recommended**)<br>• 1.0: Forward KL (mode-seeking, more focused) |
+| `--beta` | float | 0.5 | [0.0, 1.0] | Divergence interpolation coefficient<br>• 0.0: Forward KL <br>• 0.5: JSD (balanced)<br>• 1.0: Reverse KL |
 | `--lmbda` | float | 0.5 | [0.0, 1.0] | On-Policy learning trigger probability<br>• 0.0: Pure Off-Policy<br>• 0.5: Mixed strategy (**recommended**)<br>• 1.0: Pure On-Policy |
 | `--seq_kd` | bool | False | True/False | Whether to use teacher-generated sequences<br>• False: Use dataset when not on-policy<br>• True: Use teacher generation when not on-policy |
 | `--temperature` | float | 0.9 | > 0 | Generation sampling temperature, controls randomness |
@@ -201,3 +201,14 @@ swift rlhf \
 ```
 
 Training script reference [here](https://github.com/modelscope/ms-swift/tree/main/examples/train/multimodal/rlhf/gkd/fast.sh)
+
+
+## On-Policy Distillation
+We can achieve the [On-Policy Distillation](https://thinkingmachines.ai/blog/on-policy-distillation/) training described in the Thinking Machines Lab blog by setting the following parameters:
+
+```bash
+--lmbda 1 # on-policy
+--beta 1 # reverse
+```
+
+For a complete implementation, refer to the example script [here](https://github.com/modelscope/ms-swift/tree/main/examples/train/on_policy_distillation.sh).

docs/source_en/Instruction/GRPO/AdvancedResearch/index.rst

@@ -7,4 +7,5 @@ Advanced Research
    DAPO.md
    deepeyes.md
    GSPO.md
+   RLOO.md
    CHORD.md

examples/train/on_policy_distillation.sh

@@ -0,0 +1,41 @@
+# On-Policy Distillation https://thinkingmachines.ai/blog/on-policy-distillation/
+
+# CUDA_VISIBLE_DEVICES=7 \
+# swift rollout \
+#     --model Qwen/Qwen3-8B-Base \
+#     --vllm_max_model_len 24192
+
+NPROC_PER_NODE=7 \
+PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6 \
+swift rlhf \
+    --rlhf_type gkd \
+    --model Qwen/Qwen3-8B-Base \
+    --teacher_model Qwen/Qwen3-32B \
+    --train_type full \
+    --dataset open-thoughts/OpenThoughts3-1.2M#10000 \
+    --seq_kd false \
+    --lmbda 1 \
+    --beta 1 \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --learning_rate 1e-5 \
+    --gradient_accumulation_steps 1 \
+    --save_steps 1000 \
+    --save_total_limit 2 \
+    --logging_steps 1 \
+    --max_length 16000 \
+    --max_completion_length 8192 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --save_only_model true \
+    --dataloader_num_workers 64 \
+    --dataset_num_proc 4 \
+    --deepspeed zero2 \
+    --teacher_deepspeed zero3 \
+    --attn_impl flash_attn \
+    --use_vllm true \
+    --vllm_mode server \
+    --vllm_server_host 127.0.0.1 \
+    --vllm_server_port 8000