whw06

MIRA-Text-Group2

README

License: apache-2.0

TL;DR

MIRA is a source-aware data selection framework for heterogeneous mid-training corpora. Instead of applying a single global quality rubric, MIRA (1) clusters sources into capability-coherent groups, (2) lets a frontier teacher (Kimi-K2.6) freely propose rubric dimensions and anchors them per group, (3) distills the anchored teacher into a lightweight per-group student scorer, and (4) applies reliability-aware aggregation with per-source retention thresholds.

This repository is one of those student scorers — variant 2 in the Text family, specialized for Chinese identity / algorithm explanation text. Given an in-distribution record, it produces a numerical score and a short rationale for every anchor dimension in this group's rubric.

Model summary

Table

Architecture	Mixture-of-Experts decoder (35B total / ≈3B active params)
Base model	Qwen3.5-35B-A3B-Base
Fine-tuning	Full-parameter SFT on Kimi-K2.6 anchored teacher labels
Domain	Chinese flat-text identity-guided / algorithm explanation documents — `purchase_code` (distill_minimax final round), its first-round counterpart, and `ct_code_reasoning`. Strongest intra-group similarity: `purchase_code ↔ purchase_code_first = 0.959`.
Anchor rubric	15 group-specific dimensions (`group_C_dim_anchors.jsonl` in the project repo)
Source count	3 text sources
Output	Structured (score, rationale) per anchor dimension
Precision	BF16
License	Apache-2.0 (inherits from Qwen3)

Sources covered

This scorer is calibrated for the following mid-training sources in the Text / Chinese identity + algorithm QA group:

Table with columns: Source, Description
Source	Description
`purchase_code`	distill_minimax final-round purchase-code text
`purchase_code_first_round`	First-round counterpart of purchase_code
`ct_code_reasoning`	Chinese algorithm problems with worked solutions

The full source-grouping report (KMeans k=4 / 5 clusters, intra-group cosine similarities) is in the project repo.

Anchor dimensions (15 slots)

The scoring rubric for this group, discovered via Kimi-K2.6 free-form judging and clustered into 15 anchor dimensions (KMeans k=15 over the group's dim-score embeddings). Dimensions below are sorted by cluster size — larger clusters dominate the corpus and carry more signal. Anchor names are read verbatim from this group's group_C_dim_anchors.jsonl; some names recur across slots because semantically related but distinct rubric facets were clustered separately by the teacher.

Table with columns: Slot, Dimension, Cluster size
Slot	Dimension	Cluster size
A1	Reasoning Quality	37,321
A2	Instruction Following	36,571
A3	Technical Depth	31,723
A4	Bug Identification Accuracy	25,487
A5	Formatting & Structural Clarity	25,293

The scorer outputs one [Ai] <dimension>: <score>/10 — <rationale> line per slot, plus overall, training_recommendation, domain_tag, and brief.

Where this model fits in the MIRA pipeline

markdown
┌──────────────────┐  ┌──────────────────┐  ┌──────────────────┐  ┌──────────────────┐
│ 1. Rubric        │  │ 2. Anchored      │  │ 3. Reliability   │  │ 4. Data          │
│    Discovery     │→ │    Judge         │→ │    Aggregation   │→ │    Selection     │
│ (Kimi-K2.6,      │  │    Distillation  │  │ (mask unreliable │  │ (per-source      │
│  free-form       │  │ ◀── THIS MODEL   │  │  src×dim cells)  │  │  retention)      │
│  judging)        │  │                  │  │                  │  │                  │
└──────────────────┘  └──────────────────┘  └──────────────────┘  └──────────────────┘

MIRA-Text-Group2 lives in Stage 2: it scores the full Text / Chinese identity + algorithm QA corpus so that downstream stages can apply reliability masking and source-aware retention.

Intended use

Primary: Score Chinese identity / algorithm explanation text on this group's anchor dimensions to drive source-aware data selection and filtering.
Secondary: Research on rubric distillation, semantic quality scoring, and reliability diagnostics for heterogeneous training corpora.

Not intended for:

General-purpose chat or instruction following — fine-tuned to emit structured scores, not freeform dialogue.
Single-shot quality judgments without the anchor-dimension prompt template — outputs will be miscalibrated.
Records outside the Text / Chinese identity + algorithm QA group; use the matching sibling scorer instead.

Deployment

The scorer is designed to be served via vLLM behind an OpenAI-compatible endpoint and called in batch from the MIRA scoring pipeline.

1. Serve with vLLM (recommended)

bash
vllm serve whw06/MIRA-Text-Group2 \
    --tensor-parallel-size 8 \
    --dtype bfloat16 \
    --max-model-len 65536 \
    --max-num-batched-tokens 131072 \
    --gpu-memory-utilization 0.9 \
    --trust-remote-code \
    --port 8000

Why these values (verified on H200 141GB during the paper's per-source evaluation):

max-model-len=65536 — 2× the mid-training cutoff. Records can hit ~60K tokens for densely-tokenized sources; 40K runs into prompt-overflow errors.
max-num-batched-tokens=131072 — supports two full-length sequences per scheduling step.
gpu-memory-utilization=0.9 — 35B BF16 weights take ~70GB, leaving ~57GB KV cache. Roughly 4 concurrent 65K-context sequences per GPU.
8-way tensor parallel works well for the 35B MoE on a single 8×H200/A100 node.

2. Call from Python

python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

resp = client.chat.completions.create(
    model="whw06/MIRA-Text-Group2",
    messages=[
        {"role": "system", "content": SYSTEM_PROMPT},   # group-C anchor calibration
        {"role": "user",   "content": USER_PROMPT},     # record + [A1]..[A15] template
    ],
    temperature=0.7,
    top_p=0.95,
    max_tokens=2048,
)
print(resp.choices[0].message.content)

3. Prompt template

The user message asks for one structured line per anchor dimension (top-15 of this group):

markdown
[A1] {anchor_dim_1}: <score>/10 — <justification>
[A2] {anchor_dim_2}: <score>/10 — <justification>
...
[A15] {anchor_dim_15}: <score>/10 — <justification>
overall: <0-100>
training_recommendation: <keep | downsample | drop>
domain_tag: <short tag>
brief: <one-sentence summary>

The system prompt embeds the top-12 anchor calibration references (canonical examples from clustering) so the student matches the teacher's scoring scale. The full prompt builder, anchor JSONL files, and output parser are in the project repo's scoring/score_text_anchored.py.

Training details

Table

Teacher	Kimi-K2.6 (free-form rubric discovery in Phase 1; anchored re-scoring in Phase 2)
Training data	Kimi-K2.6 anchored labels on this group's Phase-2 corpus, split into a distillation set + a held-out validation split for reliability diagnostics
Loss	Standard next-token CE over (score, rationale) labels for every anchor dimension
Hyperparameters	Held constant across all MIRA student scorers; full settings in paper Appendix A.4
Validation	Per-dimension teacher–student MAE and Spearman ρ on a held-out split; dimensions failing reliability thresholds are masked post-hoc (Figure 3 in the paper)

Training loss / step curve is preserved in trainer_state.json for full reproducibility.

Headline results (from the paper)

End-to-end downstream evaluation: Qwen2.5-Coder-14B mid-trained on 25B-token MIRA-selected subsets vs. baselines, then SFT, evaluated on 9 code benchmarks across 4 categories.

Table with columns: Method, Code Gen, MultiplE, SQL (EX), SWE-Multi, Macro Avg
Method	Code Gen	MultiplE	SQL (EX)	SWE-Multi	Macro Avg
Base + SFT (no mid)	53.91	72.57	64.24	3.67	48.60
Raw Mixture (50B)	53.71	67.42	94.18	40.00	63.83
Random (25B)	52.71

MIRA-Group matches the full 50B-token raw mixture while using only half the tokens, and out-performs all 25B-token selection baselines on the macro average. This scorer is one of the 12 student models used by the MIRA-Group variant.

Sibling models

MIRA releases one student scorer per source-group variant. Use the matching scorer for each record's format:

Agent: whw06/MIRA-Agent-Group1 · -Group2 · -Group3 · -Group4
QA: whw06/MIRA-QA-Group1 · -Group2 · -Group3 · -Group4 · -Group5
Text: whw06/MIRA-Text-Group1 · ·

Limitations

MIRA addresses source-aware filtering only. Source discovery, mixture-ratio design, curriculum scheduling, deduplication and contamination control remain orthogonal concerns.
This scorer is calibrated against the Text / Chinese identity + algorithm QA group; cross-domain transfer is not advised — use the matching sibling for other source formats.
Some anchor dimensions exhibit high teacher–student MAE and are masked post-hoc during aggregation (see paper §3.4). The model still emits scores for masked dimensions; downstream consumers should re-apply the reliability mask from the project repository.
Calibrated on 3 sources within this group; behavior on out-of-distribution formats is unverified.

Citation

bibtex
@inproceedings{wang2026mira,
  title     = {MIRA: Mid-training Rubric Anchoring for Source-Aware Data Selection},
  author    = {Wang, Haowen and Du, Yaxin and Yang, Jian and Wu, Jiajun and
               Liu, Shukai and Zhang, Yuxuan and Wang, Pingjie and Chen, Siheng and
               Zheng, Tuney and Zhou, Ming and Liu, Xianglong},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year      = {2026}
}

Acknowledgments

Built on Qwen3.5-35B-A3B-Base and the Megatron-LM training stack. Teacher labels generated with Kimi-K2.6.

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