add nice selector

docs/.vuepress/notes/en/guide.ts

@@ -25,6 +25,7 @@ export const Guide: ThemeNote = defineNoteConfig({
                 'quickstart',
                 'tutorial',
                 'selector_less',
+                'selector_nice',
                 'selector_offline_tsds',
                 'selector_offline_near',
                 'selector_zeroth'

docs/.vuepress/notes/zh/guide.ts

@@ -25,6 +25,7 @@ export const Guide: ThemeNote = defineNoteConfig({
                 'quickstart',
                 'tutorial',
                 'selector_less',
+                'selector_nice',
                 'selector_offline_tsds',
                 'selector_offline_near',
                 'selector_zeroth',

docs/en/notes/guide/selector/selector_less.md

@@ -70,6 +70,7 @@ less:
     gradient_type: adam
     proj_dim: 4096
     seed: 123
+    save_interval: 16
 ```
 
 **Parameter Description:**

docs/en/notes/guide/selector/selector_nice.md

@@ -0,0 +1,229 @@
+---
+title: NICE Data Selector
+createTime: 2025/12/17 12:00:08
+permalink: /en/guide/nice/
+icon: carbon:select-02
+---
+# NICE Selector Usage Guide
+
+This document introduces how to use the **NICE Selector** for dynamic data selection in the **DataFlex** framework. The method constructs gradient similarity between the training set and the validation set: the training set uses SFT loss gradients, while the validation set uses policy gradients based on a reward model. After random projection, similarities are computed to select training samples that are most aligned with the target samples. This method is based on  
+[**NICE Data Selection for Instruction Tuning in LLMs with Non-differentiable Evaluation Metric** (ICML 2025)](https://icml.cc/virtual/2025/poster/46560).
+
+---
+
+## 1. Method Overview
+
+The core workflow of **NICE Selector**:
+
+1. **Data normalization**: Automatically supports formats such as Alpaca and ShareGPT.
+2. **Training-set gradients**: Compute gradients for each training sample and project them using TRAK.
+3. **Reward-set gradients**: Perform Monte Carlo sampling on validation data, generate responses, score them using a reward model (local vLLM or remote API), compute policy gradients toward the reward direction, and project them.
+4. **Similarity-based selection**: Align and normalize projected gradients, rank training samples by their average similarity to validation samples, and select the top-k samples for the current training round.
+
+
+## 2. Implementation Steps
+
+### Step 1: Environment Setup
+
+```bash
+git clone https://github.com/OpenDCAI/DataFlex.git
+cd DataFlex
+pip install -e .
+pip install llamafactory
+````
+
+---
+
+### Step 2: NICE Selector Configuration
+
+**Configuration file path:**
+
+```
+DataFlex/src/dataflex/configs/components.yaml
+```
+
+**Example configuration:**
+
+```yaml
+nice:
+  name: nice
+  params:
+    cache_dir: ../dataflex_saves/nice_output
+    gradient_type: adam
+    proj_dim: 4096
+    seed: 123
+    save_interval: 16
+    reward_model_backend: local_vllm   # choices: [local_vllm, api]
+    reward_backend_params:
+      local_vllm:
+        hf_model_name_or_path: meta-llama/Llama-3.1-8B
+        vllm_tensor_parallel_size: 1
+        vllm_temperature: 0.0
+        vllm_top_p: 0.9
+        vllm_max_tokens: 512
+        vllm_top_k: 40
+        vllm_seed: 42
+        vllm_max_model_len: null
+        vllm_gpu_memory_utilization: 0.9
+      api:
+        api_url: https://api.openai.com/v1/chat/completions
+        api_key: DF_API_KEY
+        model_name: gpt-4o
+        temperature: 0.0    
+    mc_samples: 4
+    max_new_tokens: 512
+    generation_temperature: 0.7
+    max_prompt_length: 4096
+```
+
+**Parameter description:**
+
+* `cache_dir`: Path to cache gradient projections and selection results; supports resuming from checkpoints.
+* `gradient_type`: `adam` (with first- and second-moment normalization) or `sgd`.
+* `proj_dim`: Random projection dimension, controlling the cost/accuracy trade-off of similarity computation.
+* `reward_model_backend`: Reward model backend; `local_vllm` uses local vLLM inference, `api` uses an HTTP service.
+* `reward_backend_params`: Backend-specific parameters.
+* `mc_samples`: Number of Monte Carlo generations per reward sample, used to stabilize policy gradient estimation.
+* `max_new_tokens` / `generation_temperature` / `max_prompt_length`: Generation length and sampling strategy for the policy model.
+
+---
+
+### Step 3: Dynamic Training Configuration
+
+**Configuration file path:**
+
+```
+DataFlex/examples/train_lora/selectors/nice.yaml
+```
+
+**Example configuration:**
+
+```yaml
+### model
+model_name_or_path: meta-llama/Llama-3.1-8B
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+lora_rank: 16
+lora_alpha: 8
+
+### dataset
+dataset: alpaca_en_demo
+template: llama3
+cutoff_len: 4096
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 0
+seed: 42
+
+### output
+output_dir: ../dataflex_saves/nice_output
+logging_steps: 10
+save_steps: 100
+plot_loss: true
+save_only_model: false
+overwrite_output_dir: true
+
+### swanlab
+report_to: none  # choices: [none, wandb, tensorboard, swanlab, mlflow]
+# use_swanlab: true
+# swanlab_project: dynamic_nice_sft
+# swanlab_run_name: name
+# swanlab_workspace: your_workspace
+# swanlab_api_key: xxxxxxx
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 1
+learning_rate: 1.0e-4
+num_train_epochs: 1.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+
+### dynamic_train
+train_type: dynamic_select
+components_cfg_file: src/dataflex/configs/components.yaml
+component_name: nice
+warmup_step: 10
+update_step: 10
+update_times: 2
+
+eval_dataset: alpaca_zh_demo
+per_device_eval_batch_size: 1
+metric_for_best_model: eval_loss
+greater_is_better: false
+load_best_model_at_end: true
+eval_strategy: steps    # choices: [no, steps, epoch]
+eval_steps: 10
+early_stopping_steps: 3
+early_stopping_min_delta: 0.01
+```
+
+**Parameter description:**
+
+* `component_name`: Must match the `nice` component in `components.yaml`, determining reward backend and projection dimensions.
+* `warmup_step` / `update_step` / `update_times`: Control the dynamic selection schedule; total steps = `warmup_step + update_step × update_times`.
+* `eval_dataset`: Validation set (Alpaca/ShareGPT style); reward model is used for scoring during generation.
+* `output_dir`: Path to save LoRA adapters and caches.
+
+---
+
+### Step 4: Run Training
+
+```bash
+FORCE_TORCHRUN=1 DISABLE_VERSION_CHECK=1 dataflex-cli train examples/train_lora/selectors/nice.yaml
+```
+
+---
+
+### Step 5: Model Merge and Export
+
+**Configuration file path:**
+
+```
+DataFlex/examples/merge_lora/llama3_lora_sft.yaml
+```
+
+**Example configuration:**
+
+```yaml
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+adapter_name_or_path: ../dataflex_saves/nice_output
+template: llama3
+trust_remote_code: true
+
+export_dir: ../dataflex_saves/Llama-3.1-8B_nice_lora_sft
+export_size: 5
+export_device: cpu  # choices: [cpu, auto]
+export_legacy_format: false
+```
+
+**Parameter description:**
+
+* `adapter_name_or_path`: Path to the LoRA adapters obtained from NICE dynamic selection training.
+* `export_dir`: Output directory for the merged full model.
+
+Run the merge and export command:
+
+```bash
+llamafactory-cli export llama3_lora_sft.yaml
+```
+
+The merged model will be saved to:
+
+```
+/dataflex_saves/Llama-3.1-8B_nice_lora_sft
+```
+
+
+## 3. Model Evaluation
+
+It is recommended to use the [DataFlow](https://github.com/OpenDCAI/DataFlow)
+[Model QA Evaluation Pipeline](https://opendcai.github.io/DataFlow-Doc/en/guide/2k5wjgls/) to systematically evaluate the generated model, and to inspect the scoring logs in `cache_dir` to analyze the reward model’s sensitivity to different samples.
+

docs/zh/notes/guide/selector/selector_less.md

@@ -67,6 +67,7 @@ less:
     gradient_type: adam
     proj_dim: 4096
     seed: 123
+    save_interval: 16
 ```
 
 **参数说明：**