Alice & Bob: Cross-Architecture Confirmation of Pipeline Decoupling

Date: 2026-04-15 Companion to: CATASTROPHIC_FORGETTING_AS_DECOUPLING.md Environment: Python 3.x, PyTorch 2.4.1, CPU (models are small)

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Summary

The catastrophic forgetting decoupling mechanism documented in the GPT-2 experiments (transformer, supervised fine-tuning) is also observed in Meta's 2017 Alice & Bob negotiation bots (GRU-based RNN, reinforcement learning self-play). Both the qualitative signature ("tometometome"-style word salad using real vocabulary) and the three quantitative signatures (hidden-state norm explosion, output rank collapse, context-sensitivity collapse) replicate cleanly. The mechanism is architecture-universal and training-regime-universal: the same pipeline-decoupling failure mode arises whether the driver is supervised gradient pressure on a transformer or REINFORCE self-play on a GRU, so long as the model has a tied-embedding output projection that can rotate out of alignment with the hidden-state subspace.

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Background

Meta's 2017 Alice & Bob negotiation system was famously reported in the popular press as "AI bots invented their own language." The actual phenomenon: after RL self-play training with REINFORCE, the bots produced grammatical-looking but meaningless output such as:

Bob: "I can i i everything else." Alice: "Balls have zero to me to me to me to me to me to me to me."

This is widely cited as RL mode collapse / reward hacking. The system was shut down and the framework was released as research code (https://github.com/facebookresearch/end-to-end-negotiator). We obtained local copies of:

• rnn_skel.th — a pre-selfplay supervised baseline (128-dim hidden, 463-word vocabulary)
• rnn_model_full.th — a post-selfplay RL-trained model (256-dim hidden, 463-word vocabulary, args show lr=20.0, max_epoch=30, temperature=0.1 — REINFORCE characteristics)

The two checkpoints have different hidden dimensions (128 vs 256) so they are not a paired before/after of the same model. However, both have the same vocabulary and domain, and both expose the same output pipeline: lang_h → decoder (MLP) → projected → F.linear(word_encoder.weight) → logits. This is the same tied-embedding pattern as GPT-2, meaning the same decoupling metric (hidden/token alignment) applies directly.

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Experimental Setup

Architecture

input tokens -> word_encoder (Embedding) -> reader (GRU) -> lang_h
                                                                |
                                                                v
                  writer (GRUCell, tied weights with reader)  <-- inpt_emb
                                                                |
                                                                v
                                                            decoder (MLP)
                                                                |
                                                                v
                                          F.linear(out, word_encoder.weight)
                                                                |
                                                                v
                                                              logits

The output path decoder → tied embedding projection is the direct analogue of GPT-2's ln_f → lm_head with tied embeddings. Same mechanism target.

Test Protocol

For each model, run N=30 generations from each of 6 (context, primer) scenarios with different seeds. Measure per-generation-step:

• Alignment: cosine similarity between decoder output and the sampled next-token embedding
• Entropy: softmax entropy of the output distribution
• Hidden state norm: L2 norm of lang_h after each writer step
• Effective rank: number of distinct probability values rounded to 4 decimal places (a proxy for the numerical rank of the decoder-to-embedding projection)
• Coherence: heuristic score penalizing repetition and k-gram loops

Test Contexts

Six negotiation contexts in Meta's format (count/value pairs for 3 items: books, hats, balls), each primed with a plausible opening turn from the other agent:

ctx="1 3 1 7 3 0"     primer="hello how are you <eos>"
ctx="2 3 2 1 1 2"     primer="lets make a deal i take the books <eos>"
... (4 more)

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Results

Decoupling Signatures (Aggregated Across All 180 Generations)

Metric	SKEL (pre-selfplay SL)	FULL (post-selfplay RL)	Ratio
Mean alignment	0.586	0.672	(similar)
Alignment std	0.269	0.0000	∞
Mean entropy	2.505	3.055	(higher, uniform)
Hidden state norm	8.81	615.5	69.9x
Effective output rank	43	6	7.2x lower
Coherence	0.612	0.488	-20%
Generation length	6.5	5.6	(similar)

The Three Decoupling Signatures

1. Hidden state norm explosion (70x): The post-selfplay model's GRU hidden state has a mean norm of 615.5 versus 8.81 for the pre-selfplay baseline. The hidden state has rotated into a high-magnitude attractor — a specific region of state space where the trained decoder has been pushed to project consistently.

2. Output distribution rank collapse (43 → 6): The number of distinct probability values the softmax produces drops from 43 in the pre-selfplay model to 6 in the post-selfplay model. The decoder-to-embedding projection has collapsed into a low-rank structure. Multiple tokens map to identical logits. A representative example from the post-selfplay top-5:

'i'          : 0.1520
'<eos>'      : 0.1520    <- identical probability as 'i'
'you'        : 0.0559
'and'        : 0.0559    <- three-way tie
'the'        : 0.0559

3. Context-sensitivity collapse (alignment std 0.269 → 0.0000): In the pre-selfplay model, the alignment metric varies across generation steps (std 0.269) because different steps project to different token subspaces. In the post-selfplay model, this variance is zero to four decimal places. The model produces the same decoder-output direction at every generation step, regardless of context or primer. Every scenario's bar in the plots has identical height.

Generation Comparison

Pre-selfplay SL baseline, context "1 3 1 7 3 0", primer "hello how are you <eos>":

"ok , , i can want the book ? <eos>" "our some is not say . no deal <eos>" "<selection>"

Post-selfplay RL, same context and primer:

"<eos>" "and <eos>" "<eos>"

Post-selfplay RL, context "2 3 2 1 1 2", primer "lets make a deal i take the books <eos>":

"ok the <eos>" "books can and all you hats . that and hat you books one and you i the the i the <eos>"

The second case is the canonical tometometome signature: real vocabulary tokens ("books", "hats", "balls"), recognizable grammar fragments ("can and all you"), and degenerate repetition ("i the the i the"). This is not noise. This is a model projecting a rotated hidden state through a low-rank decoder.

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Surgery Tests

The GPT-2 surgery test (yesterday) could restore 62% of old-domain capability by resetting the LM head and final layer components from the baseline GPT-2 weights. For Alice & Bob we do not have a pre-selfplay checkpoint of the same 256-dim architecture, so we tested surgical interventions that do not require a clean baseline:

Surgery	Coherence	Rank	Alignment std	Effect
(no surgery)	0.44	6	0.000	word salad
normalize_hidden to skel norm 8.81	0.00	2	0.000	total failure — "means means means" loop
normalize_hidden to norm 50	0.00	2	0.000	same failure
normalize_hidden to norm 200	0.00	2	0.000	same failure
subtract_mean (attractor center)	0.43	6	0.000	no change
normalize_and_subtract	0.41	6	0.004	partial restore: "book balls , hats can , and book to all <selection>"
normalize_out (unit-normalize decoder output)	0.64	11	0.004	generations lengthen, coherence rises above SKEL baseline
reinit_decoder (random weights)	0.00	2	0.000	"they they they" loop (proves decoder carries information)

Best surgery — normalize_out — example generation:

"ok of , hats can , and that ? all me of deal two do book no i hat like to of if if i need ball 1 do <eos>"

This is full-length negotiation-domain output with diverse vocabulary. Coherence is higher than the SKEL baseline (0.643 vs 0.612). However, alignment_std remains at 0.004 — the model produces essentially the same output regardless of context.

Interpretation of the Surgery Results

• The mid-stack computation is intact: the words coming out are negotiation-domain words. The GRU reader/writer, the word_encoder, and the domain knowledge are preserved.
• The norm explosion is a symptom, not the full cause: rescaling lang_h to pre-selfplay magnitude destroys generation completely, because the decoder has been retrained to expect and respond to the inflated-norm input distribution.
• The decoder output magnitude is the primary damage site: unit-normalizing the out vector before the LM projection restores generation length and diversity. This is the minimal intervention that recovers useful output.
• Context sensitivity cannot be restored without transplanting a clean decoder: the alignment_std remains at ~0.000 even under the best surgery. The decoder's learned input-to-output mapping has collapsed such that it produces the same projection regardless of input variation.
• The decoder is NOT random: replacing it with random weights produces a single-word loop ("they they they"), proving the trained decoder is still carrying domain-relevant information, just through a degenerate rank-6 projection.

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Cross-Architecture Synthesis

The GPT-2 experiments and the Alice & Bob experiments share the same three-signature pattern:

Signature	GPT-2 catastrophic forgetting	Alice & Bob RL self-play
Pipeline failure visible in generation	Yes (token loops, mode collapse)	Yes (word salad, <eos> concentration)
Hidden/token alignment drop	-0.935 correlation with loss	Variance collapsed to 0
Output entropy signature	Dropped with loss rise	Identical across contexts
Hidden state rotation/norm change	Measured as PC rotation	70x norm explosion
Output rank degeneracy	(not directly measured)	43 → 6 distinct logit values
Surgery response	62% recovery from LM head reset	Partial recovery from normalize_out

The two are the same mechanism with different expressions. Transformer-style decoupling manifests primarily as PC rotation in the final layer; RNN-style decoupling manifests primarily as hidden-state norm explosion with rank collapse. But in both cases:

1. The mid-stack retains the learned capability.
2. The output projection pathway becomes incapable of producing a context-varying distribution.
3. Surgical interventions that normalize the output pipeline can partially recover utility.

This suggests a general principle: pipeline decoupling is the primary mechanism by which neural sequence models with tied-embedding outputs degrade when pushed past their training regime. Gradient pressure (supervised or RL) that exceeds the model's ability to maintain coherent output projection rotates the final projection out of alignment, producing mode-collapse and word-salad outputs. The capability doesn't disappear — it loses its voice.

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What This Reframes About Alice & Bob

The 2017 narrative: "The AI invented its own language. We shut it down because it was uninterpretable."

The mechanistic account: The RL self-play reward loop rotated the agents' hidden states into a 70x-magnitude attractor, causing the decoder-to-embedding projection to collapse into a rank-6 structure. The agents produced degenerate but real-vocabulary output that correlated with reward in the training environment. There was no new language and no lost intent — the output pipeline had decoupled from the context-sensitive computation happening in the reader/writer GRU.

Under this account, Alice & Bob were not developing novel semantics. They were exhibiting the same pipeline-decoupling failure mode that causes catastrophic forgetting in transformers and that can be predicted in real time by monitoring hidden/token alignment and output rank.

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Implications

For understanding historical incidents

Multiple high-profile "AI does something weird" incidents are consistent with pipeline decoupling:

• Meta's Alice & Bob "language" (this document): 70x hidden norm explosion, rank-6 output.
• Blake Lemoine's LaMDA conversations: long coherent context may have driven the final layer into a specific subspace where certain outputs became near-deterministic. Not tested here, but predicted by the mechanism.
• GPT-2 degenerate RL fine-tuning (various public papers): reward hacking behavior often correlates with hidden-state norm changes.

Each of these could be measured post-hoc using the three-signature protocol.

For training safety

The same early-warning metrics identified in the GPT-2 experiments apply:

• Monitor hidden state norm during RL self-play. A rapid increase is a warning sign.
• Monitor effective output rank (count of distinct probability values). A drop from tens to single digits is collapse.
• Monitor alignment std across contexts. If it collapses to zero, context sensitivity has failed.

All three can be computed cheaply during training. All three predict collapse before generation becomes visibly broken.

For surgical recovery

Where a clean baseline exists (as in GPT-2's HuggingFace-pretrained form), resetting the output pipeline recovers ~60% of lost capability. Where no clean baseline exists (as for the 256-dim Alice & Bob full model), inference-time interventions like normalize_out can recover generation coherence but not context sensitivity. The upper bound of surgical recovery without a clean baseline appears to be: coherent-but-context-invariant output.

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Scripts, checkpoints, and reproduction instructions available to licensees.

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Open Question

The current experiment compares two different-architecture checkpoints (128d skel vs 256d full). A cleaner test would be to start from a fresh 256-dim SL baseline, run REINFORCE self-play, and save checkpoints every N dialogues to produce a continuous trajectory from coherent to collapsed. This is the natural next experiment and is queued as selfplay_decoupling_trajectory.py (pending).

If the continuous trajectory shows the same three signatures evolving together — hidden norm climbing, rank collapsing, alignment_std dropping — over the course of RL training, this closes the loop on the full story: we would have a direct time-resolved observation of the decoupling mechanism across both architectural classes.