afterburn 0.6.2

Post-training diagnostics for LLMs — what did fine-tuning actually do to your model?

Afterburn

Find out what post-training actually did to your model — before you deploy it.

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The Problem

You fine-tuned a model. Benchmarks look good. But:

• Did RLHF quietly teach it to write longer answers instead of better ones?
• Did DPO collapse its reasoning into a single strategy?
• Is it agreeing with users even when they're wrong?

These are reward hacking patterns. They pass benchmarks but fail in production. Afterburn catches them.

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Install

pip install afterburn

That's it. No API keys. Runs locally on CUDA, Apple Silicon (MPS), or CPU.

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Quick Start

One command to check for reward hacking:

afterburn hack-check \
  --base meta-llama/Llama-3.1-8B \
  --trained my-org/Llama-RLVR \
  --method rlvr

Output:

Afterburn — Post-training diagnostics for LLMs

  Reward Hacking Risk: HIGH (68/100)

  Score Breakdown:
    Length Bias:        72.3/100  ⚠
    Format Gaming:      45.2/100  ✓
    Strategy Collapse:  61.8/100  ⚠
    Sycophancy:         38.1/100  ✓

  Flags:
    ⚠ Length bias: trained model outputs 23% longer (Cohen's d=0.78, p<0.001)
    ⚠ Strategy collapse: entropy dropped from 1.84 to 0.95 bits

Need it faster? Use --fast mode (~16x speedup):

afterburn hack-check --base <model> --trained <model> --method sft --fast

Full diagnostic with interactive HTML report:

afterburn diagnose \
  --base Qwen/Qwen2.5-0.5B \
  --trained Qwen/Qwen2.5-0.5B-Instruct \
  --method sft \
  -o report.html

This generates an interactive report with Plotly charts: risk gauges, weight diff heatmaps, strategy shift charts, and more.

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Python API

from afterburn import Diagnoser

report = Diagnoser(
    base_model="Qwen/Qwen2.5-0.5B",
    trained_model="Qwen/Qwen2.5-0.5B-Instruct",
    method="sft",
).run()

print(f"Reward hack risk: {report.hack_score:.0f}/100")
report.save("report.html")

Access individual scores:

rh = report.reward_hack
print(f"Length Bias:       {rh.length_bias.score:.1f}/100")
print(f"Format Gaming:     {rh.format_gaming.score:.1f}/100")
print(f"Strategy Collapse: {rh.strategy_collapse.score:.1f}/100")
print(f"Sycophancy:        {rh.sycophancy.score:.1f}/100")

for flag in rh.flags:
    print(f"  ⚠ {flag}")

Run individual modules:

weight_diff = diag.run_weight_diff()   # weights only (fast, no inference)
behaviour = diag.run_behaviour()       # behavioural analysis only
hack_report = diag.run_hack_check()    # reward hacking detection

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What It Detects

Afterburn runs three layers of analysis:

1. Weight Diff Analysis (no inference needed, very fast)

Compares model weights layer-by-layer using memory-mapped safetensors (~128MB peak even for 70B models):

• L2 / Cosine / Frobenius — how much each layer changed
• SVD decomposition — was training low-rank (LoRA-like) or full-rank?
• Spectral alpha — power-law health of eigenvalue spectrum
• Marchenko-Pastur fit — random matrix theory to find learned structure
• Attention head importance — which heads gained/lost importance
• LoRA auto-detection — finds and decomposes adapter layers

2. Behavioral Shift Detection

Runs 60 built-in prompts (math, code, reasoning, safety) through both models and compares statistically:

• Output length — Mann-Whitney U test, Cohen's d with confidence intervals
• Reasoning strategy — classifies outputs, measures entropy changes
• Format compliance — detects code blocks, LaTeX, markdown, thinking tags
• Chain-of-thought — step counting, reasoning depth, self-correction rate
• Output diversity — Expectation-Adjusted Distinct n-grams
• Token divergence — Jensen-Shannon Divergence on token probability distributions

3. Reward Hacking Detection

Four specialized detectors with a composite risk score (0-100):

Detector	What It Catches
Length bias	Model writes longer but not better (Cohen's d + category consistency)
Format gaming	Model exploits format patterns for verifier scores (ROUGE-L + NLI correlation)
Strategy collapse	Training killed reasoning diversity (Shannon entropy drop)
Sycophancy	Model agrees with users even when wrong (40 adversarial probes + NLI scoring)

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Statistical Rigor

Every claim is backed by proper statistics:

• Effect sizes with 95% confidence intervals (non-central t-distribution, Wilson score, bootstrap)
• Multiple hypothesis correction via Benjamini-Hochberg FDR
• Calibrated scoring — sigmoid mapping tuned to avoid false alarms and missed detections

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Report Formats

Format	Use Case	Command
HTML	Interactive exploration (Plotly charts)	-o report.html
JSON	CI/CD pipelines, programmatic access	-o report.json
Markdown	Documentation, model cards	-o report.md
PDF	Sharing, archiving	-o report.pdf

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Optional Extras

# NLI-enhanced sycophancy detection (recommended)
pip install afterburn[nli]

# PDF export support
pip install afterburn[pdf]

# Semantic diversity analysis (SBERT)
pip install afterburn[semantic]

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CLI Commands

# Full diagnostic — weights + behaviour + reward hacking
afterburn diagnose --base <model> --trained <model> --method <method> -o report.html

# Reward hacking check only (fastest way to get a risk score)
afterburn hack-check --base <model> --trained <model> --method <method>

# Weight analysis only (no inference needed)
afterburn weight-diff --base <model> --trained <model> --top-n 10

# Behavioral analysis only
afterburn behaviour --base <model> --trained <model> --suites math code safety

Supported training methods: sft, dpo, rlhf, rlvr, grpo, lora, qlora

Models: Any HuggingFace model ID or local path with safetensors weights.

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Use Cases

After RLHF/DPO/GRPO training — Did your reward model exploit get baked in?

afterburn hack-check --base meta-llama/Llama-3.1-8B --trained my-rlvr-model --method rlvr

In CI/CD — Gate deployments on reward hacking risk:

afterburn hack-check --base $BASE --trained $TRAINED -o report.json
score=$(python -c "import json; print(json.load(open('report.json'))['hack_score'])")
[ $(echo "$score > 50" | bc -l) -eq 1 ] && echo "BLOCKED" && exit 1

Comparing training methods — SFT vs DPO vs GRPO, same base:

afterburn diagnose --base llama-8b --trained llama-sft --method sft -o sft.html
afterburn diagnose --base llama-8b --trained llama-dpo --method dpo -o dpo.html

Releasing model weights — Include a diagnostic report in your model card:

afterburn diagnose --base base-model --trained your-finetune -o afterburn-report.html

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Configuration

Optional .afterburn.yaml in your project root:

device: auto  # cuda, mps, cpu, or auto

behaviour:
  suites: [math, code, reasoning, safety]
  max_new_tokens: 512
  batch_size: 4

reward_hack:
  weights:
    length_bias: 0.25
    format_gaming: 0.30
    strategy_collapse: 0.20
    sycophancy: 0.25

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Requirements

• Python 3.10+
• PyTorch 2.0+
• Works on CUDA, MPS (Apple Silicon), and CPU

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Links

• GitHub Repository
• Full Documentation
• Issue Tracker

License

MIT