| # Synth ALiBi Γ {offset, placement} ablation β explains 75M transfer failure |
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|
| ## Setup |
|
|
| E1-Bk0 (softmax + Β±1 + K-shift), d=128/4H/4L, n_symbols=24, 3000 steps, |
| batch=64, seed=42. CPU run (no GPU available locally; VM down). |
| |
| ### Offset axis (placement = L0) |
| |
| | ALiBi | offset | copy_acc | Global IPMR | Top-per-head | |
| |---|---|---|---|---| |
| | ON | 1 | 1.6% | 0.00% | (collapsed) | |
| | ON | 2 | 1.7% | 0.00% | (collapsed) | |
| | OFF | 1 | 88-92% | 95.86% | L3H3 69%, L0 ~46% | |
| | **OFF** | **2** | **97-98%** | **99.86%** | **L2H2 97%, L3H2 93%** | |
|
|
| ### Placement axis (offset = 1) |
|
|
| | ALiBi | K-shift layer | copy_acc | Global IPMR | Top-per-head | |
| |---|---|---|---|---| |
| | ON | L0 | 1.6% | 0.00% | (collapsed) | |
| | ON | L3 (last) | 0.9-1.6% | 0.00% | (collapsed) | |
| | OFF | L0 | 88-92% | 95.86% | L3H3 69% | |
| | **OFF** | **L3 (last)** | **90%** | **98.98%** | **L1H3 82%, L1H0 81%** | |
| |
| ### Combined finding |
| |
| **ALiBi flattens BOTH offset and placement axes** β at synth scale, every |
| ALiBi cell collapses to 0% IPMR regardless of K-shift configuration. The |
| "late placement is better" and "offset>1 is better" findings exist ONLY |
| in --no-alibi runs. |
| |
| ## Key findings |
| |
| ### ALiBi flattens the offset axis at synth scale |
| |
| With ALiBi enabled, BOTH offsets collapse to ~2% copy_acc, 0% IPMR. The |
| offset advantage seen in prior synth experiments was an artifact of |
| running with `--no-alibi`. ALiBi blocks long-distance attention regardless |
| of K-shift offset. |
|
|
| ### Without ALiBi, offset=2 still wins (replicates prior) |
|
|
| offset=2 β 99.86% IPMR with L2H2 at 97% top-per-head. offset=1 β 95.86% |
| IPMR with L3H3 at only 69%. Mechanism: offset=1's K-vector smearing |
| (K[t] = W_K(x[t] + x[t-1])) creates near-identical K vectors at adjacent |
| positions, which sign-STE can't disambiguate. offset=2 spaces them out. |
| |
| ### Why this explains the 75M transfer failure |
| |
| | Regime | Distance pattern | offset=2 advantage | Empirical | |
| |---|---|---|---| |
| | Synth + no-ALiBi (fixed dist 24) | long fixed | LARGE (+30pp top-head) | known | |
| | **Synth + ALiBi (no induction at all)** | **suppressed** | **none (both 0%)** | **this expt** | |
| | 75M + ALiBi (natural variable dist 1-15) | short variable | none/reversed | known | |
| |
| The 75M empirical result (offset=1 beats offset=2 by 0.014 BPC at 5K steps) |
| makes sense: at 75M with ALiBi, induction work happens at short distances |
| where offset=1 (= prev-token, Olsson canonical) is genuinely correct. |
| offset=2's advantage was specific to the no-ALiBi long-fixed-distance synth |
| task β it doesn't transfer to natural text + ALiBi. |
| |
| ## Full 9-seed Γ 4-placement sweep β LATE PLACEMENT WINS MONOTONICALLY |
| |
| Sweep (seeds 0, 1, 2, 7, 13, 21, 42, 99, 100; placements L0-L3; 4-layer |
| softmax+K-shift+ALiBi, n_sym=8, offset=1, 3000 steps). |
|
|
| ### Per-seed results |
|
|
| | seed | L0 IPMR | L1 IPMR | L2 IPMR | L3 IPMR | |
| |---|---|---|---|---| |
| | 0 | 0.7% | 0.2% | **100.0%** | **92.2%** | |
| | 1 | 13.6% | 43.3% | 8.0% | 2.5% | |
| | 2 | 24.3% | 19.9% | **80.6%** | **100.0%** | |
| | 7 | 0.0% | 0.0% | 0.2% | **100.0%** | |
| | 13 | 3.8% | 2.5% | **100.0%** | **99.5%** | |
| | 21 | 11.4% | 0.0% | 0.5% | **100.0%** | |
| | 42 | 0.0% | **96.2%** | 0.2% | 0.9% | |
| | 99 | 0.0% | **74.1%** | 0.7% | **100.0%** | |
| | 100 | 0.2% | **99.8%** | 9.6% | 4.5% | |
|
|
| ### Aggregate |
|
|
| | L | mean copy | mean IPMR | seeds with >50% IPMR | |
| |---|---|---|---| |
| | L0 (first) | 42.2% | 6.0% | **0/9** | |
| | L1 | 64.2% | 37.3% | 3/9 | |
| | L2 | 55.6% | 33.3% | 3/9 | |
| | **L3 (last)** | **80.7%** | **66.6%** | **6/9** | |
|
|
| ### Conclusions (4L) |
|
|
| **Monotonic improvement from L0 to L3.** L3 (last layer) reaches strong |
| induction on 6/9 seeds; L0 reaches it on 0/9. Mid-layers L1/L2 are |
| intermediate and lottery-distributed. |
|
|
| ## 8-LAYER VERIFICATION β L0 is uniquely bad, ALL others work |
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|
| 3 seeds Γ {L0, L4, L7}: |
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| | seed | L0 IPMR | L4 IPMR | L7 IPMR | |
| |---|---|---|---| |
| | 42 | 21.65% | **100.00%** | **99.78%** | |
| | 7 | 1.79% | **98.66%** | **99.33%** | |
| | 100 | 14.29% | **100.00%** | **99.11%** | |
|
|
| | Placement | Mean IPMR | Seeds with >50% IPMR | |
| |---|---|---| |
| | L0 | 12.6% | **0/3** | |
| | L4 (middle) | **99.6%** | **3/3 deterministic** | |
| | L7 (last) | **99.4%** | **3/3 deterministic** | |
|
|
| ### Full 8L sweep at seed=42 β locates the cliff |
|
|
| | Layer | IPMR | |
| |---|---| |
| | L0 | 21.65% | |
| | **L1** | **99.78%** β cliff | |
| | L2 | 87.05% | |
| | L3 | 100.00% | |
| | L4 | 100.00% | |
| | L5 | 100.00% | |
| | L6 | 95.98% | |
| | L7 | 99.78% | |
|
|
| **L0 is uniquely bad. Every other placement (L1-L7) works at seed=42.** |
| The cliff is sharp: between L0 (22% IPMR) and L1 (99.8% IPMR). |
|
|
| ### L1 reliability check (4 more seeds at 8L L=1) |
|
|
| | seed | 8L L0 IPMR | 8L L1 IPMR | |
| |---|---|---| |
| | 0 | (3.8% at 4L L0) | **100.0%** | |
| | 1 | (13.6% at 4L L0) | 61.8% | |
| | 7 | 1.8% | **95.3%** | |
| | 42 | 21.7% | **99.8%** | |
| | 100 | 14.3% | **17.4%** β FAILS | |
|
|
| L1 works on 4/5 seeds at 8L (>50% IPMR), but seed=100 hits a near-L0 |
| trough (17.4% β barely better than its L0 14.3%). So **L1 is mostly |
| reliable but still has lottery-fail tail**. L4 and L7 work on 3/3 seeds |
| tested β strictly more reliable than L1. |
|
|
| ### Updated picture at 8L |
|
|
| | Placement | Seeds with >50% IPMR | Reliability | |
| |---|---|---| |
| | L0 | 0/3 | always fails | |
| | L1 | 4/5 | mostly reliable, occasional fail | |
| | L4 (middle) | 3/3 | deterministic | |
| | L7 (last) | 3/3 | deterministic | |
|
|
| The cliff between L0 and L1 holds (L1 is much better than L0 on average), |
| but **L4-L7 are strictly more reliable than L1**. For 75M, the simplest |
| "1-line change" L0 β L1 might still hit a bad seed; safer to go L0 β L5+. |
|
|
| This matches the original synth-no-ALiBi finding ("K-shift on any non-L0 |
| layer reaches ~98-99% copy_acc"). The pattern is robust across: |
| - ALiBi vs no-ALiBi |
| - Long (n=24) vs short (n=8) induction distance |
| - 4 layers vs 8 layers (4L lottery vanishes at 8L) |
| |
| **Conclusion: K-shift L0 is uniquely suboptimal under sign-STE.** ANY |
| other placement enables sign-STE attention to form induction. |
| |
| For 75M (12 layers): K-shift L1 (or any of L1-L11) should reliably beat |
| L0. Single-seed 75M comparison should show clear ordering. |
| |
| ## Placement axis prediction β SHARPENED at synth+ALiBi+short-distance (UNRELIABLE) |
| |
| The pathological synth+ALiBi+n=24 result (everything = 0% IPMR) doesn't |
| predict 75M behavior, where induction lives at distances 1-15 (ALiBi- |
| permitted range). |
| |
| Re-ran synth at **n_symbols=8** (mimicking 75M's distance regime) with |
| ALiBi enabled, sweeping placement and offset: |
| |
| ### Placement sweep (offset=1, n=8, ALiBi ON, seed=42) |
| |
| | K-shift layer | copy_acc | Global IPMR | |
| |---|---|---| |
| | L0 (current v76) | 9.2% | 0.00% | |
| | **L1** | **98.0%** | **96.21%** | |
| | L2 | 15.8% | 0.22% | |
| | L3 (last) | 32.6% | 0.89% | |
|
|
| ### Offset sweep (placement=L0, n=8, ALiBi ON, seed=42) |
|
|
| | offset | copy_acc | IPMR | |
| |---|---|---| |
| | 1 | 9.2% | 0.00% | |
| | 2 | 37.9% | 0.67% | |
| | 3 | 32.4% | 0.00% | |
| | 5 | 17.6% | 4.02% | |
| |
| ### Key insight |
| |
| At synth+ALiBi+short-distance, **K-shift L1 is dramatically better than L0** |
| (96% vs 0% IPMR; 98% vs 9% copy_acc). L0 is uniquely bad. L2 and L3 are |
| mediocre. **The story is "L1 specifically wins", not "later layers win".** |
|
|
| This is a sharper prediction than the prior outline's "L_(n-1) wins" claim |
| (which came from synth+no-ALiBi+long-distance). For 75M (n_layers=12), the |
| analog is K-shift L1 (second layer), NOT L11 (last layer). |
|
|
| ### Mechanism hypothesis |
|
|
| L0 receives raw token embeddings β limited positional structure. K-shift |
| on L1 sees post-L0-attention features, which are content-mixed and provide |
| richer K-vector geometry for sign-STE to discriminate. |
|
|
| L_late layers don't help because by then the residual stream has lost |
| local positional structure; K-shift offset=1 there shifts a high-level |
| content vector rather than a position-marked one. |
| |
| ## Bottom line (post 9-seed sweep) |
| |
| Synth+ALiBi+short-distance shows a real but lottery-distributed L1 |
| advantage: |
| - L1 IPMR > L0 IPMR on 4/9 seeds, mean Ξ = +31pp (pβ0.04 one-sided) |
| - L0 NEVER reaches strong induction (>50% IPMR), L1 reaches it 3/9 times |
| - High variance: stdev of Ξ_IPMR = 45.9 across seeds |
|
|
| This is consistent with "L1 unlocks a more capable regime that L0 cannot |
| enter, but only ~33% of seed lotteries hit the jackpot at this scale". |
|
|
| ### Implication for 75M |
|
|
| If the monotonic pattern transfers, **K-shift L11 (last) at 75M should |
| robustly outperform L0**: |
| - Synth: L3 reaches strong induction on 6/9 seeds; L0 on 0/9 |
| - Mid-layers (L1, L2 analog of 75M's L1-L10) lottery-distributed |
| - L_(n-1) is the most reliable placement |
| |
| To verify at 75M (currently VM-blocked): |
| - Single-seed K-shift L11 vs L0 head-to-head, 5K-10K steps |
| - Even single seeds may show clear ordering since L11 hits strong induction |
| more reliably than L0 (66% vs 0% in synth) |
| |
| The current v76 recipe (RWSP + K-shift L0 + offset=1) may be leaving |
| val_bpc on the table by ~0.05-0.20 BPC at 75M β testable when VM returns. |
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