---
license: mit
tags:
  - mechanistic-interpretability
  - nla
  - gsm8k
  - qwen
  - thought-anchors
  - sentence-embeddings
---

# nla-thought-anchors-rollouts

Sentence-level NLA rollout dataset for Qwen2.5-7B-Instruct on GSM8K test set.

For each activation vector (layer 20, 1319 examples), **40 NLA descriptions** were sampled from the actor `kitft/nla-qwen2.5-7b-L20-av` (temperature=0.7). Each description was split into sentences and scored with the NLA critic `kitft/nla-qwen2.5-7b-L20-ar`.

Part of the [nla-thought-anchors](https://github.com/Realmbird/nla-thought-anchors) project.

## Files

| File | Extraction point | Rows |
|---|---|---|
| `hash.parquet` | `####` token (model finished CoT, about to write answer) | 175,240 |
| `answer.parquet` | Answer digit token (answer now in context) | ~175,000 |

## Schema

| Column | Type | Description |
|---|---|---|
| `example_idx` | int32 | Index in GSM8K test split |
| `is_correct` | bool | Whether model answer matched gold |
| `gold_answer` | string | Ground-truth answer |
| `rollout_idx` | int32 | 0–39 — which of the 40 sampled descriptions |
| `sentence_idx` | int32 | Sentence position within this rollout |
| `sentence_text` | string | The sentence text |
| `sentence_embedding` | list\<float32\> len=384 | MiniLM-L6-v2 embedding |
| `cos_sim_to_full_nla` | float32 | Cos-sim of sentence to its own rollout's full description |
| `critic_cos_sim` | float32 | NLA critic reconstruction cos-sim vs original activation |
| `critic_mse` | float32 | NLA critic reconstruction MSE vs original activation |
| `contains_gold_answer` | bool | Sentence contains the gold answer number |
| `contains_pred_answer` | bool | Sentence contains the predicted answer number |

## Key findings

**Reconstruction quality by correctness** (hash position):

| Group | Mean critic cos-sim |
|---|---|
| Correct examples | 0.604 |
| Incorrect examples | 0.416 |

Incorrect examples reconstruct ~35% worse — the activation at `####` is qualitatively different when the model gets the answer wrong.

**Thought anchor analysis**: sentences that are stable across all 40 rollouts (high cos-sim to cross-rollout centroid) reconstruct significantly better than peripheral sentences:

| Group | Correct | Incorrect |
|---|---|---|
| Peripheral sentences (Q1 anchor score) | 0.503 | 0.366 |
| Anchor sentences (Q4 anchor score) | 0.661 | 0.673 |

The most striking result: **anchor sentences from incorrect examples (0.673) reconstruct better than peripheral sentences from correct examples (0.503)** — the stable recurring content is what carries the activation signal regardless of correctness.

**Anchor score** is not stored in this dataset — compute it post-hoc from `sentence_embedding` by grouping rows by `example_idx`, computing the centroid of all embeddings, and taking cos-sim of each sentence to the centroid.

## Usage

```python
from datasets import load_dataset
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

ds = load_dataset("Realmbird/nla-thought-anchors-rollouts", split="hash")

# compute anchor scores for each example
import pandas as pd
df = ds.to_pandas()
embs = np.stack(df["sentence_embedding"].values)

anchor_scores = np.zeros(len(df))
for ex_idx, group in df.groupby("example_idx"):
    idx = group.index
    centroid = embs[idx].mean(axis=0)
    centroid /= np.linalg.norm(centroid) + 1e-12
    sims = cosine_similarity(embs[idx], centroid.reshape(1, -1)).flatten()
    anchor_scores[idx] = sims

df["anchor_score"] = anchor_scores
```

## Related

- Step 1 activations (#### token): `Realmbird/nla-thought-anchors-hash`
- Step 1 activations (answer token): `Realmbird/nla-thought-anchors-answer`
- Step 2 NLA descriptions: `Realmbird/nla-thought-anchors-hash-step2`
- NLA actor: `kitft/nla-qwen2.5-7b-L20-av`
- NLA critic: `kitft/nla-qwen2.5-7b-L20-ar`