metadata
license: mit
tags:
- coconut
- activation-oracle
- interpretability
- chain-of-thought
- latent-reasoning
- gpt2
datasets:
- synthetic
language:
- en
base_model:
- openai-community/gpt2-large
pipeline_tag: text-generation
Cocoracle: Activation Oracles for Coconut Latent Reasoning
Checkpoints from the Cocoracle experiment -- interpreting what a model "thinks" during latent reasoning.
Combines Coconut (Chain of Continuous Thought) with Activation Oracles to train models that answer natural-language questions about their own latent chain-of-thought hidden states.
Models
GPT-2-large Coconut (all-latent)
stage3_alllatent.pt -- GPT-2-large (774M) fine-tuned with the Coconut curriculum to perform multi-digit addition using entirely latent reasoning.
- Task: Multi-digit addition (2-4 digits) with carry propagation
- Accuracy: 45.4% (teacher-forced) on all-latent reasoning
- Architecture: GPT-2-large + 4 special tokens (
<bot>,<sep>,<eot>,<act>)
GPT-2-large Self-Oracle (all-latent)
self_oracle_alllatent.pt -- The Coconut model further fine-tuned to interpret its own latent reasoning activations via norm-matched injection at layer 17.
- CoT exact match: 6.9%
- CoT token F1: 34.2%
- Random baseline: 0%
Usage
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token
tokenizer.add_special_tokens({
"additional_special_tokens": ["<bot>", "<sep>", "<eot>", "<act>"]
})
model = GPT2LMHeadModel.from_pretrained("gpt2-large")
model.resize_token_embeddings(len(tokenizer))
state = torch.load("stage3_alllatent.pt", map_location="cpu")
model.load_state_dict(state)
See the GitHub repo for full code and an interactive demo (scripts/interactive.py).
Results
| Configuration | CoT Exact Match | CoT Token F1 | AO Val Loss |
|---|---|---|---|
| Separate AO (GPT-2-small + LoRA) | 0% | 26.4% | 2.92 |
| Self-oracle, GPT-2-small | 0% | 32.5% | 1.98 |
| Self-oracle, GPT-2-large, stage 1 | 0% | 25.6% | 1.10 |
| Self-oracle, GPT-2-large, all-latent | 6.9% | 34.2% | 0.55 |
References
- Hao et al., arXiv:2412.06769
- Karvonen et al., arXiv:2512.15674