Datasets:
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license: apache-2.0
language:
- en
task_categories:
- text-generation
tags:
- math
- reinforcement-learning
- tool-use
- hindsight
configs:
- config_name: source
data_files:
- split: full
path: rl_trace_opsd.parquet
- config_name: grpo
default: true
data_files:
- split: train
path: grpo/train.parquet
- split: validation
path: grpo/validation.parquet
rl_trace_opsd
The versioned mathematical RL data used by the ToolElite TRACE-OPSD project.
The source contains 20,161 examples, with columns id, problem,
ground_truth, difficulty, hindsight, and solution. Difficulty labels are
mid (10,940 examples) and high (9,221 examples).
Two representations of the same examples
| Configuration | Split | Examples | Purpose |
|---|---|---|---|
source |
full |
20,161 | Full problem pool, including held-out validation IDs |
grpo |
train |
19,905 | Immutable training split in ToolElite/verl format |
grpo |
validation |
256 | Fixed training-monitoring validation split |
These are different representations of the same pool, not additional datasets.
Do not train on the source/full split without excluding the fixed validation
IDs. The validation split has 160 mid + 96 high examples; split seed is 42.
It is separate from final GSM8K/MATH500/AIME benchmark evaluation.
from datasets import load_dataset
training = load_dataset("BitZone910/rl_trace_opsd", "grpo", split="train")
validation = load_dataset("BitZone910/rl_trace_opsd", "grpo", split="validation")
source = load_dataset("BitZone910/rl_trace_opsd", "source", split="full")
The GRPO format includes the tool prompt, terminal answer reward, source IDs, and training-only privileged hindsight. At inference/evaluation, expose only the problem and tool prompt to the policy; do not leak answer or hindsight fields.
Source and attribution
Derived from NuminaMath-TIR, whose publisher declares Apache-2.0. ToolElite adds difficulty labels, solution hindsight, filtering, and tool-training formatting. Preserve upstream attribution and the included Apache license when redistributing.