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configs:
- config_name: ai2-arc-ARC-Easy
default: true
data_files:
- split: train
path: ai2-arc-ARC-Easy/pending.jsonl.gz
- config_name: ai2-arc-ARC-Challenge
data_files:
- split: train
path: ai2-arc-ARC-Challenge/pending.jsonl.gz
- config_name: Multi-subject-RLVR
data_files:
- split: train
path: Multi-subject-RLVR/pending/part-*.jsonl.gz
- config_name: trivia-qa
data_files:
- split: train
path: trivia-qa/pending.jsonl.gz
- config_name: WebInstruct-verified
data_files:
- split: train
path: WebInstruct-verified/pending/part-*.jsonl.gz
- config_name: MMLU-Pro
data_files:
- split: validation
path: MMLU-Pro/pending.jsonl.gz
- config_name: SuperGPQA
data_files:
- split: validation
path: SuperGPQA/pending.jsonl.gz
---
# GLM-5.2 CoT trajectory generation inputs
This repository contains only the seven datasets currently in scope. Each
dataset is placed directly under the repository root.
Download the complete package with:
```bash
huggingface-cli download liaialley/la-data \
--repo-type dataset \
--local-dir la-data
```
## Generation contract
- Send each row's `query` to GLM-5.2 unchanged.
- Save the model's native `reasoning_content` and `content` separately.
- Do not add another system prompt or require JSON, `<think>`, or `\boxed{}` in
the generation request.
- Add `<think>...</think>`, concatenate the two response fields, and normalize
final-answer boxing only in deterministic post-processing when the training
or evaluation pipeline requires it.
- Accept a trajectory only when its extracted final answer matches `label`;
otherwise resample.
## Input alignment
- A dataset's original system prompt is retained when one is published.
- When no original system prompt exists, `fallback_system_prompt.txt` is used.
- Multi-subject-RLVR publishes the same text as the selected fallback.
- No dataset is given an additional `\boxed{}` instruction.
- MMLU-Pro reproduces the official category-specific few-shot user prompt.
- SuperGPQA reproduces the official reasoning-model zero-shot user prompt.
- MMLU-Pro and SuperGPQA have no official system role, so the shared fallback is
prepended while their official user-level benchmark prompts remain unchanged.
The current Qwen branch of `src/stage1/data.py` still hard-codes a boxed system
prompt. It must be updated before training so runtime input uses the system
message stored in each row's `query`.
Each dataset directory contains `pending.jsonl.gz`; the two largest training
sets use equivalent `pending/part-*.jsonl.gz` shards for reliable transfer and
parallel processing. All earlier MMLU-Pro and SuperGPQA trajectories were reset
because they were generated under a different request/output protocol; both
benchmarks must be regenerated uniformly.
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