BOTS reference evaluation results collection

examples/bots/README.md

@@ -33,6 +33,25 @@ Also refer to the [Data Preparation Guide](https://github.com/LLM360/Reasoning36
 
 Remember to modify the model/data path in `bots.yaml` and `random.yaml` accordingly.
 
+##### (Optional) Customize Reference Evaluation Results
+
+Modify `ref_eval_collect.yaml` to set the reference model you want to evaluate, e.g., Qwen2.5-1.5B-Instruct.
+
+Launch evaluation by executing:
+```bash
+BOTS_REF_EVAL_LOG_FILE="path/to/save/eval/logs" trinity run --config examples/bots/ref_eval_collect.yaml --plugin-dir examples/bots/workflow
+```
+
+The evaluation logs will be saved at the specified location. Then integrate the evaluation results as a new column into the original dataset:
+
+```bash
+python examples/bots/ref_eval_collect.py \
+--data-path <your/path/to/original/dataset> \
+--ref-eval-path <your/path/to/bots_ref_eval_log.jsonl> \
+--ref-eval-key <column name, e.g., qwen2.5_1.5b_pass_rate>
+```
+Remember to update `task_selector.feature_keys` in `bots.yaml`.
+
 ##### Step 3: Training
 Launch training by executing:
 ```bash

examples/bots/README_zh.md

@@ -30,6 +30,26 @@ BOTS 以任务选择、模型训练和后验概率更新的连续循环运行。
 请参考LLM360提供的[数据准备指南](https://github.com/LLM360/Reasoning360?tab=readme-ov-file#data-preparation)和[技术报告](https://www.arxiv.org/pdf/2506.14965)。
 请修改`bots.yaml`和`random.yaml`中相应的模型/数据路径。
 
+
+##### （可选）客制参考评估结果
+
+修改 `ref_eval_collect.yaml` 以设置你想要评估的参考模型，例如Qwen2.5-1.5B-Instruct。
+
+执行以下命令启动评估：
+```bash
+BOTS_REF_EVAL_LOG_FILE="path/to/save/eval/logs" trinity run --config examples/bots/ref_eval_collect.yaml --plugin-dir examples/bots/workflow
+```
+
+评估日志会保存在指定的路径下。接下来将评估结果作为新列聚合到原数据集：
+
+```bash
+python examples/bots/ref_eval_collect.py \
+--data-path <your/path/to/original/dataset> \
+--ref-eval-path <your/path/to/bots_ref_eval_log.jsonl> \
+--ref-eval-key <column name, e.g., qwen2.5_1.5b_pass_rate>
+```
+记得修改`bots.yaml`中的`task_selector.feature_keys`字段。
+
 ##### 第三步：训练
 执行以下命令启动训练：
 ```bash

examples/bots/bots.yaml

@@ -24,7 +24,7 @@ buffer:
     taskset:
       name: math-train
       storage_type: file
-      path: '<DATA_ROOT>/LLM360/guru-RL-92k/train/math__combined_54.4k.parquet'
+      path: 'your/data/path/containing/math__combined_54.4k.parquet' # you need to set it manually
       split: 'train'
       format:
         prompt_key: 'prompt'
@@ -44,7 +44,7 @@ buffer:
     eval_tasksets:
     - name: math-eval
       storage_type: file
-      path: '<DATA_ROOT>/LLM360/guru-RL-92k/online_eval/math__math_500.parquet'
+      path: 'your/data/path/containing/math__math_500.parquet' # you need to set it manually
       format:
         prompt_key: 'prompt'
         response_key: 'reward_model.ground_truth'

examples/bots/random.yaml

@@ -20,7 +20,7 @@ buffer:
     taskset:
       name: math-train
       storage_type: file
-      path: '<DATA_ROOT>/LLM360/guru-RL-92k/train/math__combined_54.4k.parquet'
+      path: 'your/data/path/containing/math__combined_54.4k.parquet' # you need to set it manually
       split: 'train'
       format:
         prompt_key: 'prompt'
@@ -32,7 +32,7 @@ buffer:
     eval_tasksets:
     - name: math-eval
       storage_type: file
-      path: '<DATA_ROOT>/LLM360/guru-RL-92k/online_eval/math__math_500.parquet'
+      path: 'your/data/path/containing/math__math_500.parquet' # you need to set it manually
       format:
         prompt_key: 'prompt'
         response_key: 'reward_model.ground_truth'

examples/bots/ref_eval_collect.py

@@ -0,0 +1,33 @@
+import argparse
+import json
+
+import numpy as np
+import pandas as pd
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data-path", type=str, required=True)
+    parser.add_argument("--ref-eval-path", type=str, required=True)
+    parser.add_argument("--ref-eval-key", type=str, required=True)
+    args = parser.parse_args()
+
+    print(f"Loading original dataset from {args.data_path}...")
+    original_data = pd.read_parquet(args.data_path)
+    prompt2linenum = {}
+    for i, d in enumerate(original_data["prompt"]):
+        prompt2linenum[d[0]["content"]] = i
+    eval_results = [0.0 for _ in range(len(original_data))]
+    print(f"Loading reference evaluation results from {args.ref_eval_path}...")
+    print(f"Results will be written to the original dataset at a new column {args.ref_eval_key}...")
+    with open(args.ref_eval_path, "r") as f:
+        for line in f:
+            item = json.loads(line)
+            eval_results[prompt2linenum[item["question"][0]["content"]]] = np.mean(item["rewards"])
+    original_data[args.ref_eval_key] = eval_results
+    print(f"Dataset overwritten at {args.data_path}...")
+    original_data.to_parquet(args.data_path)
+
+
+if __name__ == "__main__":
+    main()

examples/bots/ref_eval_collect.yaml

@@ -0,0 +1,59 @@
+project: "bots_ref_eval_collect_demo_1.5B"
+name: "run-1"
+mode: explore
+checkpoint_root_dir: ${oc.env:TRINITY_CHECKPOINT_ROOT_DIR,./checkpoints}
+algorithm:
+  algorithm_type: grpo
+  repeat_times: 16
+  optimizer:
+    lr: 1e-6
+model:
+  model_path: ${oc.env:TRINITY_MODEL_PATH,Qwen/Qwen2.5-1.5B-Instruct}
+  max_prompt_tokens: 4096
+  max_response_tokens: 8192
+cluster:
+  node_num: 1
+  gpu_per_node: 8
+buffer:
+  total_epochs: 1
+  batch_size: 100
+  explorer_input:
+    taskset:
+      name: math-train
+      storage_type: file
+      path: 'your/data/path/containing/math__combined_54.4k.parquet' # you need to set it manually
+      split: 'train'
+      format:
+        prompt_key: 'prompt'
+        response_key: 'reward_model.ground_truth'
+      rollout_args:
+        temperature: 1.0
+      task_selector:
+        selector_type: sequential
+    default_workflow_type: 'bots_ref_eval_collect_math_boxed_workflow'
+  trainer_input:
+    experience_buffer:
+      name: exp_buffer
+      storage_type: queue
+      path: 'sqlite:///bots_ref_eval_collect_buffer.db'
+explorer:
+  eval_interval: 100
+  runner_per_model: 16
+  rollout_model:
+    engine_num: 8
+    tensor_parallel_size: 1
+    enable_prefix_caching: false
+    enforce_eager: true
+    dtype: bfloat16
+    seed: 42
+synchronizer:
+  sync_method: 'nccl'
+  sync_interval: 10
+  sync_timeout: 1200
+trainer:
+  trainer_type: 'verl'
+  save_interval: 1000
+  grad_clip: 1.0
+  use_dynamic_bsz: true
+  max_token_len_per_gpu: 24576
+  ulysses_sequence_parallel_size: 1

examples/bots/workflow/bots_math_boxed_workflow.py

@@ -1,5 +1,9 @@
-from typing import Union
+import fcntl
+import json
+import os
+from typing import List, Union
 
+from trinity.common.experience import Experience
 from trinity.common.workflows.customized_math_workflows import MathBoxedWorkflow, Task
 from trinity.common.workflows.workflow import WORKFLOWS
 
@@ -21,6 +25,48 @@ def format_messages(self):
         return self.task_desc
 
 
+@WORKFLOWS.register_module("bots_ref_eval_collect_math_boxed_workflow")
+class BOTSRefEvalCollectMathBoxedWorkflow(MathBoxedWorkflow):
+    """A reference evaluation collection workflow for math tasks that give answers in boxed format for BOTS."""
+
+    def reset(self, task: Task):
+        super().reset(task)
+        from trinity.plugins.bots_math_boxed_reward import BOTSMathBoxedRewardFn
+
+        self.reward_fn = BOTSMathBoxedRewardFn(**self.reward_fn_args)
+        self.task_desc = nested_query(self.format_args.prompt_key, self.raw_task)
+        self.truth = nested_query(self.format_args.response_key, self.raw_task)
+
+    def format_messages(self):
+        # the prompts are already in message format
+        return self.task_desc
+
+    def run(self) -> List[Experience]:
+        responses = super().run()
+
+        rewards = [response.reward for response in responses]
+
+        log_entry = {
+            "model_version": self.model.model_version,
+            "rewards": rewards,
+            "question": self.task_desc,
+            "truth": self.truth,
+        }
+
+        log_file_path = os.environ.get("BOTS_REF_EVAL_LOG_FILE", "./bots_ref_eval_log.jsonl")
+        os.makedirs(os.path.dirname(log_file_path), exist_ok=True)
+
+        with open(log_file_path, "a") as f:
+            fcntl.flock(f, fcntl.LOCK_EX)
+            try:
+                json.dump(log_entry, f)
+                f.write("\n")
+            finally:
+                fcntl.flock(f, fcntl.LOCK_UN)
+
+        return responses
+
+
 def nested_query(query_key: str, query_obj: Union[dict, None]):
     # support nested query for a dict given query_keys split by '.'
     if query_obj is None: