XingYing-stack/TIPS-Qwen3-4B-Instruct-2507-Math
Text Generation • 4B • Updated
data_source stringclasses 4
values | prompt listlengths 2 2 | ability stringclasses 1
value | reward_model dict | extra_info dict |
|---|---|---|---|---|
AgentProcessBench/bfcl | [
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"content": "You are a strict but fair trajectory annotator for tool-use agents.\n\nYou will be given one complete trajectory consisting of system, user, assistant,\nand tool messages, together with the tool definitions.\n\nYour task is to label EACH assistant message (each assistant message constitutes\none S... | agent_process_judge_orm | {
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AgentProcessBench/bfcl | [
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"content": "You are a strict but fair trajectory annotator for tool-use agents.\n\nYou will be given one complete trajectory consisting of system, user, assistant,\nand tool messages, together with the tool definitions.\n\nYour task is to label EACH assistant message (each assistant message constitutes\none S... | agent_process_judge_orm | {
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"... |
AgentProcessBench/bfcl | [
{
"content": "You are a strict but fair trajectory annotator for tool-use agents.\n\nYou will be given one complete trajectory consisting of system, user, assistant,\nand tool messages, together with the tool definitions.\n\nYour task is to label EACH assistant message (each assistant message constitutes\none S... | agent_process_judge_orm | {
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AgentProcessBench/bfcl | [{"content":"You are a strict but fair trajectory annotator for tool-use agents.\n\nYou will be give(...TRUNCATED) | agent_process_judge_orm | {
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} | {"assistant_indices":[3,5,7,9,12,14,16,18,20,22,24,26],"dataset_name":"bfcl","final_label":1,"first_(...TRUNCATED) |
AgentProcessBench/bfcl | [{"content":"You are a strict but fair trajectory annotator for tool-use agents.\n\nYou will be give(...TRUNCATED) | agent_process_judge_orm | {
"ground_truth": -1,
"style": "rule"
} | {"assistant_indices":[3,5,7,10,12,14,17],"dataset_name":"bfcl","final_label":-1,"first_neg1_index":-(...TRUNCATED) |
AgentProcessBench/bfcl | [{"content":"You are a strict but fair trajectory annotator for tool-use agents.\n\nYou will be give(...TRUNCATED) | agent_process_judge_orm | {
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} | {"assistant_indices":[3,5,7,9,11,13,15,17,19,21,23,26,28,30,32,34,37,39,41,43,46,48,50],"dataset_nam(...TRUNCATED) |
AgentProcessBench/bfcl | [{"content":"You are a strict but fair trajectory annotator for tool-use agents.\n\nYou will be give(...TRUNCATED) | agent_process_judge_orm | {
"ground_truth": -1,
"style": "rule"
} | {"assistant_indices":[3,5,7,9,11,13,16,18,20,23,25,28,30,33,35,37,39,41,43,45],"dataset_name":"bfcl"(...TRUNCATED) |
AgentProcessBench/bfcl | [{"content":"You are a strict but fair trajectory annotator for tool-use agents.\n\nYou will be give(...TRUNCATED) | agent_process_judge_orm | {
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} | {"assistant_indices":[3,5,7,9,11,13,16,18,20,22,24,26,28,30,32,35,37],"dataset_name":"bfcl","final_l(...TRUNCATED) |
AgentProcessBench/bfcl | [{"content":"You are a strict but fair trajectory annotator for tool-use agents.\n\nYou will be give(...TRUNCATED) | agent_process_judge_orm | {
"ground_truth": -1,
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} | {"assistant_indices":[3,5,7,9,11,14,16,18,20,22,24,26,28,30,32,35,37,39,41,44,46],"dataset_name":"bf(...TRUNCATED) |
AgentProcessBench/bfcl | [{"content":"You are a strict but fair trajectory annotator for tool-use agents.\n\nYou will be give(...TRUNCATED) | agent_process_judge_orm | {
"ground_truth": -1,
"style": "rule"
} | {"assistant_indices":[3,5,7,9,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,43,45,47,49,51,53,55,57,5(...TRUNCATED) |
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TIPS Training Data
Outcome-labeled training trajectories used by TIPS (Thinking-Induced Process Supervision).
| Configuration | File | Examples |
|---|---|---|
| Math | math/train.parquet |
3,200 |
| Agent | agent/train.parquet |
2,905 |
TIPS trains a generative reward model to produce a reasoning chain, step-level labels, and an outcome label while using only outcome correctness as the reinforcement-learning reward.
Usage
from datasets import load_dataset
math_data = load_dataset("XingYing-stack/TIPS-Training-Data", "math", split="train")
agent_data = load_dataset("XingYing-stack/TIPS-Training-Data", "agent", split="train")
Data preparation and training code are available at https://github.com/RUCBM/TIPS.
Sources and Licenses
The math data is derived from SCAN-Pro, released under Apache-2.0. The agent data consists of rollout trajectories collected by us. Please also follow the terms of the original math data source.
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