1541 code-critic DPO preference pairs, filtered to fit in 8192 tokens

.gitattributes

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README.md

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+---
+license: apache-2.0
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- dpo
+- preference
+- process-reward-model
+- code-critic
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: prm_dpo_pairs_train.jsonl
+---
+
+# PRM_1541i
+
+1541 preference pairs for training a critic over coding-agent trajectories. Each example is a
+multi-turn agent transcript paired with two candidate critiques — one preferred, one rejected.
+
+A critique is a structured error analysis over 12 categories, each with a `DETECTED: Yes/No` verdict
+and, when detected, `EVIDENCE` and `RECOVERY_ACTION` fields, closing with `TASK_STATUS` and
+`OVERALL_GUIDANCE`.
+
+## Format
+
+ShareGPT / LLaMA-Factory layout (`dataset_info.json` included):
+
+```json
+{
+  "messages":  [{"role": "system", ...}, {"role": "user", ...}, ...],
+  "chosen":    {"role": "assistant", "content": "SPECIFICATION ERRORS:\n1. ..."},
+  "rejected":  {"role": "assistant", "content": "SPECIFICATION ERRORS:\n1. ..."}
+}
+```
+
+`messages` ends on a user turn; `chosen` and `rejected` are single assistant messages. To use with
+TRL's conversational preference format:
+
+```python
+from datasets import load_dataset
+
+def to_preference(example):
+    return {
+        "prompt": example["messages"],
+        "chosen": [example["chosen"]],
+        "rejected": [example["rejected"]],
+    }
+
+dataset = load_dataset("code-critic-model/PRM_1541i", split="train")
+dataset = dataset.map(to_preference, remove_columns=["messages"])
+dataset = dataset.train_test_split(test_size=0.1, seed=42)  # 1386 train / 155 eval
+```
+
+## Length filtering
+
+Pre-filtered so that every pair fits whole in **8192 tokens** under the
+`Qwen/Qwen3-4B-Instruct-2507` tokenizer, measured the way `DPOTrainer` measures it (render with the
+chat template, then tokenize `prompt` and `prompt + completion`). Nothing is truncated or dropped at
+train time. Prompts are long — around 7000 tokens — and completions are around 580.
+
+## Statistics
+
+Measured over all 1541 pairs:
+
+| | |
+|---|---|
+| pairs | 1541 |
+| median prompt length | ~7000 tokens |
+| median completion length | ~580 tokens |
+| `OVERALL_GUIDANCE` share of completion | 12.5% (median 74 tokens) |
+| pairs where chosen/rejected flip >= 1 `DETECTED` verdict | 892 (57.9%) |
+| pairs differing in prose only (identical verdicts) | 649 (42.1%) |
+| median flipped `DETECTED` labels per pair | 1 |
+| median char similarity, pre-`OVERALL_GUIDANCE` section | 0.399 |
+| pairs whose common prefix reaches `OVERALL_GUIDANCE` | 8.2% |
+
+The last three rows matter for anyone planning to slice this data. The chosen and rejected critiques
+are **not** identical up to the guidance paragraph — they diverge a median of ~1859 characters before
+`OVERALL_GUIDANCE` begins, and the categorization sections differ substantially. Training only on the
+`OVERALL_GUIDANCE` span would discard ~87% of the tokens and most of the signal.
+
+Slicing by pair type does not help either: in a DPO run on this data, the verdict-flip subset scored
+0.620 held-out preference accuracy and the prose-only subset 0.618 — statistically indistinguishable.
+
+## Models trained on this dataset
+
+- [`code-critic-model/Qwen3-4B-DPO-beta0.1-sft0.25-lr1e-6-bs32-ep1`](https://huggingface.co/code-critic-model/Qwen3-4B-DPO-beta0.1-sft0.25-lr1e-6-bs32-ep1)
+  — 0.619 held-out preference accuracy vs 0.516 for the base model.

dataset_info.json

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+{
+  "prm_dpo_pairs_train": {
+    "file_name": "prm_dpo_pairs_train.jsonl",
+    "formatting": "sharegpt",
+    "ranking": true,
+    "columns": {
+      "messages": "messages",
+      "chosen": "chosen",
+      "rejected": "rejected"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant",
+      "system_tag": "system"
+    }
+  }
+}

prm_dpo_pairs_train.jsonl

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