Loop transformer halting line, 2026-09-24 snapshot: 79 runs, 91 model checkpoints, evaluations, probes

loop_transformer_halting_2026-09-24/README.md

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+# Loop transformer, depth generalisation from depth <= 2 labels (2026-09-24 snapshot)
+
+Checkpoints of the loop / halting experiment line of https://github.com/physicsru/Composition_in_Transformer (`toy_model/`). Every run directory holds
+model-only checkpoints (`ckpt_<update>.pt`, `best.pt` = best shallow validation, `model_<update>.pt` = model extracted from `last.pt` of a run that was still
+training when this snapshot was taken), `manifest.json` (frozen configuration incl. `model` = constructor arguments and the training flags), `final_eval.json`
+(full test matrix: 3 categories x 8 depths x 5,000 chains, main protocol + forced R = d + fixed-budget grid), `metrics.jsonl` (monitoring every 5k updates) and
+`train_log.jsonl`. `probes/` holds the stability-probe outputs (`probe_*.out`) and the basin sweeps (`*.json`, see `scripts/consist_range_probe.py`).
+`runs_index.json` lists the checkpoints per run. Data: `data/chain_loop_k8` (base world: 500 entities, 20 relations, k = 8) and `data/chain_{w43,g2,R40,E1000}_k8`
+in the GitHub repo (regenerate with `src/generate_chain.py`; see `toy_model/docs/index_halting_runs.md`).
+
+## Loading
+
+```python
+import json, torch, sys; sys.path.insert(0, "toy_model/src")
+from model.loop_gpt import LoopGPT
+m = json.load(open("halt_CS128_k8_s1/manifest.json"))["model"]; vocab = json.load(open("data/chain_loop_k8/vocab.json")); meta = json.load(open("data/chain_loop_k8/meta.json"))
+e0, r0 = vocab.index("<e_0>"), vocab.index("<r_0>")
+model = LoopGPT(len(vocab), m["d_model"], m["n_head"], m["n_layer"], pos=m["position"], attn_window=m["attn_window"], reembed=m["reembed"],
+                ent_range=(e0, e0 + meta["num_entities"]), rel_range=(r0, r0 + meta["num_relations"]), untied=bool(m.get("untied")),
+                untied_group=(m.get("untied") or {}).get("group", 1), nr_init=bool(m.get("nr_init")), ready_state=bool(m.get("ready_state")), inject=bool(m.get("inject")))
+model.load_state_dict(torch.load("halt_CS128_k8_s1/ckpt_0200000.pt", map_location="cpu", weights_only=False)["model"]); model.eval()
+# a chain x r1 ... rd is answered by running R = d loops and reading the last position:  see train_halt.eval_fixed
+```
+
+## What the runs are (log sections refer to toy_model/docs/experiments_halting_log.md)
+
+| run family | description | result |
+|---|---|---|
+| `halt_CS128_k8_s*`, `halt_CS128d128_k8_s*` | the main result: standard 4-block loop, RoPE, anchored states + one-step consistency (constructed depth 32 / 128) + random-entity prefix 0-128, depth <= 2 labels only | d = 2..128 all 1.000, 0 errors / 120,000 on 5 of 6 seeds (§13.13) |
+| `halt_CS128na`, `halt_CSnp`, `halt_CS32`, `halt_AO2rope128`, `halt_AO2rope32` | one-variable ablations of the recipe: no anchor / no prefix / prefix 0-32 / anchor only | §13.9-13.15 |
+| `halt_CS128_{w43,g2,R40,E1000}_k8_s*` | the recipe in four other worlds; R40 has all three checkpoints (100k before the rise, best = 150k at the peak, 200k after the late regression) | §13.15-13.20 |
+| `halt_CS128d128r32_k8_s1` | 2 x 2 completion (constructed depth 128, 32 rows) | 1.000 |
+| `halt_HS1_k8_s*`, `halt_Dk8re3_dec_s*` | stateless re-embedding loop (architectural prior) with / without learned halting | 0 errors, stops at T = d (§7.4, §9) |
+| `halt_VE32_CS128`, `halt_VL4_CS128`, `halt_VL4_plain` | vanilla controls of the recipe (32 different layers = 8 steps of 4; 4 layers; plain 4-layer) | d >= 3 exactly 0 (§13.11-13.14) |
+| `halt_DPLt`, `halt_DPLs`, `halt_DP8` | data-only solutions (depth labels 3..32 truth / self-generated, 3..8) | range-limited 'clocked' solution (§11, §14.1) |
+| `halt_RL8v`, `halt_RL8s`, `halt_RL32`, `halt_RL8prm` (`model_<update>.pt`) | RL arms (GRPO with the model's own stopping), still training at the snapshot | §15 |
+| `halt_CS128_R40c64`, `halt_CS128_E1000c64`, `halt_DL64` (`model_<update>.pt`) | remedies / data-only arms still training at the snapshot | §13.24 |

loop_transformer_halting_2026-09-24/halt_AO2rope128_k8_s1/ckpt_0200000.pt

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loop_transformer_halting_2026-09-24/halt_AO2rope128_k8_s1/final_eval_best.json

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loop_transformer_halting_2026-09-24/halt_AO2rope128_k8_s1/manifest.json

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loop_transformer_halting_2026-09-24/halt_AO2rope128_k8_s1/metrics.jsonl

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loop_transformer_halting_2026-09-24/halt_AO2rope128_k8_s123/manifest.json

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loop_transformer_halting_2026-09-24/halt_AO2rope128_k8_s123/metrics.jsonl

@@ -0,0 +1,23 @@
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loop_transformer_halting_2026-09-24/halt_AO2rope128_k8_s7/final_eval_best.json

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loop_transformer_halting_2026-09-24/halt_AO2rope128_k8_s7/manifest.json

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loop_transformer_halting_2026-09-24/halt_AO2rope128_k8_s7/metrics.jsonl

@@ -0,0 +1,23 @@
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loop_transformer_halting_2026-09-24/halt_AO2rope32_k8_s1/final_eval_best.json

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loop_transformer_halting_2026-09-24/halt_AO2rope32_k8_s1/manifest.json

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loop_transformer_halting_2026-09-24/halt_AO2rope32_k8_s1/metrics.jsonl

@@ -0,0 +1,23 @@
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loop_transformer_halting_2026-09-24/halt_AO2rope32_k8_s123/final_eval_best.json

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loop_transformer_halting_2026-09-24/halt_AO2rope32_k8_s123/manifest.json

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loop_transformer_halting_2026-09-24/halt_AO2rope32_k8_s123/metrics.jsonl

@@ -0,0 +1,23 @@
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loop_transformer_halting_2026-09-24/halt_AO2rope32_k8_s7/final_eval_best.json

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loop_transformer_halting_2026-09-24/halt_AO2rope32_k8_s7/manifest.json

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loop_transformer_halting_2026-09-24/halt_AO2rope32_k8_s7/metrics.jsonl

@@ -0,0 +1,23 @@
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loop_transformer_halting_2026-09-24/halt_CS128_E1000_k8_s1/ckpt_0200000.pt

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