diff --git a/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/README.md b/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/README.md
new file mode 100644
index 0000000000..fc9eb537cb
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/README.md
@@ -0,0 +1,30 @@
+# Record: MuonEq-R + Context-Only SLOT + QK_GAIN=5.0
+
+**val_bpb: 1.1027** (3-seed mean, std 0.0011) | ~15.80 MB | 8xH100 SXM | ~88.8ms/step | ~6654 steps
+
+Built on PR #1179 (@dexhunter) with three additions:
+
+- **MuonEq-R** (row-normalization before Newton-Schulz) -- from arXiv:2603.28254
+- **QK_GAIN_INIT=5.0** -- our hyperparameter sweep (monotonic gains from 1.5 to 5.0)
+- **Context-Only SLOT** -- causal variant of SLOT that optimizes delta using only already-scored context tokens
+
+## 3-Seed Results
+
+| Seed | Context-SLOT BPB | TTT BPB | Steps | ms/step | Artifact |
+|------|-----------------|---------|-------|---------|----------|
+| 1337 | **1.10166** | 1.11008 | 6660 | 88.8 | 15,795,518 |
+| 42 | **1.10378** | 1.11206 | 6650 | 88.9 | 15,793,163 |
+| 2024 | **1.10271** | 1.11108 | 6653 | 88.9 | 15,796,779 |
+| **Mean** | **1.10272 +/- 0.00106** | 1.11107 | 6654 | 88.8 | 15,795,153 |
+
+Beats merged SOTA (PR #1019, 1.1147) by **0.012 BPB** (p << 0.01).
+
+## Reproduction
+
+```bash
+pip install brotli
+QK_GAIN_INIT=5.0 SLOT_ENABLED=1 SLOT_STEPS=8 SLOT_LR=0.005 SEED=$SEED \
+  torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
+
+Training: ~600s. Eval (sliding + context-only SLOT): ~190s. Total: ~13 min end-to-end.
diff --git a/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/submission.json b/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/submission.json
new file mode 100644
index 0000000000..65f82d3f29
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/submission.json
@@ -0,0 +1,19 @@
+{
+  "author": "bigbag",
+  "name": "MuonEq-R + Context-Only SLOT + QK_GAIN=5.0",
+  "date": "2026-04-01",
+  "track": "10min_16mb",
+  "val_bpb": 1.10271599,
+  "val_bpb_std": 0.00105969,
+  "seeds": [1337, 42, 2024],
+  "seed_results": {
+    "1337": {"val_bpb": 1.10165738, "artifact_bytes": 15795518, "steps": 6660, "step_avg_ms": 88.75},
+    "42": {"val_bpb": 1.10377675, "artifact_bytes": 15793163, "steps": 6650, "step_avg_ms": 88.90},
+    "2024": {"val_bpb": 1.10271384, "artifact_bytes": 15796779, "steps": 6653, "step_avg_ms": 88.85}
+  },
+  "bytes_total": 15796779,
+  "code_bytes": 22718,
+  "hardware": "8xH100 80GB SXM",
+  "base_pr": 1179,
+  "technique_summary": "MuonEq-R optimizer + Context-Only SLOT + QK_GAIN=5.0 + Brotli compression"
+}
diff --git a/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/train_gpt.py b/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/train_gpt.py
new file mode 100644
index 0000000000..18b56a7ec2
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/train_gpt.py
@@ -0,0 +1,2 @@
+import lzma as L,base64 as B
+exec(L.decompress(B.b85decode(";TF|KnOy)g9Eu`uszf$Krci>25I-HuUNjF`N?9VI&1P%41Wt3M0HcnGi85w-CJ8_DYWqCUhZ{>g4nvkJcgHT-RDPNuEud#fAnQaLSNvkmp$G^X{d(AZjRFxFy>2wf?R)sc9i|_R`TJuej!(A6t?t2t=+vR+ec}DB7#mA!0U3ZPbPd!4VG0zt<mgm>IP9Y`Qh|Y5(~*gPUu|L4Sm$iKcMKwI^Xm?&;!d4_(?pkoFq&`({X<p6<RxlkcxQ0&2;cwx6?hk3*)Ee5h(;!E+O#eeSyMhFD~;;=)_um7J#?>;YasmKijVu_E?FVOr018tgy@qyX@~WH+K-{#(pQ3sorfjje*Nqq`A<W-k}*tDPN5<hE}}4ru_E7Yy03%?Ks=<eKAD<M-B{5Bq+(Cxey#zkH%r@S`e-aNv9Gefe2K_1S0;XdLh1O={-?Jat0WgbolQlCIqmPOz^9qH^8L~67x-RTVrp)Am)r(;c56{?AX)h|()WPH&xdh{O~vLCLYWiD?Q#`mKK3*uV@)6J%HBuT&?(D04`{M?hH98rb7(fxYl@GgASafVr!=N4$v;L~mUvG60r7IppV)ozz}9hMZo|bWQ<sdk(0L<)=Z2TR?s83^2$B*i2Nm&1k7l<JAfjpQTTJ=W7A9?5bereX2R7il_hPJPyYq^FQoFd)4Ek^i1Y#&nxuLBw@p(&LWR`PfYpzI+nR1t&258@776@j#3s&ntg(uhWUVX5V{AL3H6@W33*&%i~0I`KO)F2Ic&nFebOP=7+HF0RR<oA1Pg{#^|5<I9j9d;_}Co;Ugo*f!=FR1b#<d+Xk$f1)F49KV;cdpUr(5})5j}0;O<hnJv0C6eaJx~$uInhw(GtGPz7x|n1dOzqL;H#5jVFs3+jyb~s;H44A|GzixXLcbO>jnAH#2oqxm<dtbD~f+>Y=ERi&#v}WlLw)|zW>xaD0R(Xii$i~3lIJSNYhuO;!r(0jIG}Mj)~w>w<o>{F9*RCJ?e-N=g~oga2Dww6Q(2ZQnkf1d)+V-e+yFgmA)BGt=5*Ym<40o?9<_oU}`tCF`w=UrX(o<_#FV!AcE#rhRFf|eRF~3Cjq5wZ=`#2v$GndICM8;50c{)-(6Pvm6W1aAqaI7KfqNozgRm8V*(YZGw*&-o><sNY9q`y<{wM2soQB(GqmuSDu%DFRWPiZ<g}BEo`W^cYIxp%x!}L`nwITX!-|Osx$JfT1L>D$c<FiS)28DYQRSsl&=}rfm-S--L>r~1a(o`+us6bu$Dk<nk8KTpQ!I87!e|JGj81vMrSN`_{yforiZtrK$3|!*>pQd1Cn&2q85g02HR^skp|5m`b5?2gKu-HF<A;Gc!r-|<2Aqs%FIY9e1WZyBb&V88a4EC2QFC-+9?LH$#9_8~+n~yjds5$PCr|5gjNwi3fqD`~KY#_Qepg>d2ygiroiK(k`X~d1yS3C?oyQLL1J10OdE=d958oL1P4tcVKW;f)cF^7gQ=Zv3e0GnxN);;yWTj_Ge0G+07^HQcg%}14d>ww#&%HCEtU6^UkHU;t50#Z0tB+=G)x1HK>6(Jj%9FHqpGhOK2oqlkrCXFz!3w+ibOxHCTcmP%5-b*AErx0=^6N=n_ol^4;hQ1=*Gz_l_2ve;*DDCf+=6B><yn1R1*qT?MYQcstgreH#Jn>^c3YU@<~&j$lrD#^=|V!JNXSbQ18=nd-7u8c<FyC4B4NDCA79q70T#R-Uucw}=5<kcxbteBFHTfM%kz%hfXJXN$${e87K`8Ok0d$gzHv^WF-bbKV+^b98aMTPo0+zm%s5l&rr+3~K+XXGX6prP!MmvX3a~63n#de`0i9-m76~li$Q@g<CEB|L@h{}`1=5{d2QX;9)$}@?6>ifrEg=&ND-sSl<HuwX@xO+Gww?1Q0z|sRPe;@s@dXRgQ#2$<_%iPhH2KB0LVq3(;<o8sa;-6yfw|aoMLg4M^C*NxnU5rep*{k_RU5XJ&>YV>x0vL!1n8$Ei^xW%QH+<dN;eG7ApeGt;CEcZ+jMJba=zX(cJ{v9O{vu){9eU}U2=Y($R)B+3e#32YqlrIMiJo`1k);iwk<8Hwtpa?rRxOW0)qRp<2(XSCp0FEc)X}XE&F6M+|2vVEr@hdtRI`O?`Ra7G;=qgXj1b}S7fH3vS*{;A^Mg+qxx>hEvsZvJllS3yr>snOw+lTjIj`{z@UxNNTtQ1Ck^prKdoM{F`;>LPCyV`#dJwgp^Vav9}&kM$~E6Ty}O1uyAK+sUr{ggHgmN>FZ-KhFbAhPeQRwh4_S;KPThZo(3UZc<#VkCL5XBbg#JSsWw?CMeEp;zh<XB-wXaGm3R<jp<jmUn+O1Xiey8{;(gM|3Tnpp*V|?U@snvP7Dnv^YFt{_O%!ee(cy)G%oli@O?u7;5vr>hSxe|Q=J%YQz>mobcRCy$IC32LAHtkys%;npjvY!O(1W#l8lkn(p2*}i=k8yNF{k2UVOC%ALD^`AKo|R*-u&V82Y1gjHL$+&r1tVk?Lm!3mJ&rm`xN`V$!?8G|^7ez8HN-fHN(y6hHy=V{1YbD^D7-F(`(Q`{M5DbR4|TUozLQwV=_tXYi-M^C1G7pNbohKEjpf{gf`6li`WV`&pW@-&G2$ti)*@Q3Djks|%AaZX)t5r7o{cAHk@dIIMhY!%oreu6!J&o@0)WfPJF*xa8Q@>%BIVl%EB2{90cxcM>aHc)ZN>Y;RC|4d5qv0B2d!07YzFIA{$eU1?E+aYl97Ik$AfzqoQ<Yw%nVh#<EF&1*R_L4HOwIxv`dq`!80sa?!*O2vf!F<>pzKzx!6cHIQGPkIbkK09lOwS*vY3O>An2+w-2Yo#@Eg$IXV{ga_?#)Al9FNu!7}K+U)`k`t;E@;V>C8luA%?`fg@M;??|;eaM{|Rjt{cr8Jp<y=5j%_+Pt{+GVK0YB=*f9=REb{;p|eSs@FTWxkEscBF81HROmT%<*{BIfw(5)Qc&@_7X?qr)crcHL^xq{SovaWK#AOivoh)7<-{OxKTqj?Hp`TC_FACb=9xRJVXtwwp_jCdBQxA9f*_K@g2BSX|-F`5aKJq1j10?RI>zn757f65;B0+T8y1kgRCb=Dv?ThR`heT)a*j+3<`vp9LW454gEbcL3sQ`iR4Vf@TUGw5N7QfmAP!g16qGeV6$x4;&sPhii)s!63xnl{XZ&&Plk*-^6M`);d47O3kfCzr_%<SFqllW$R0mxxG0@dhdDdmzJoUeO&KM(UV6)nLZtbi$O&b^S*BLOL54JKj3d55{6A^xjk?yc^7e-jmPq$hq@{Mp_x;DocngI#CufDOY%)W?Lr4}Av#UJ;1h~p2)`q8ieBcAt(aw)sy*)tv;p&~cw6EI8V@wy}{Sw>UpH<zi^|Zv6VM&${dY3w4gyYW#G!vi^YYC`g4&IfH!lvrL5p!F+%^|?-CMG1}P8lLjP8*O)m*g?3qpq(*Np`lws{rXANLndlaGtCSdu**@qEe14XY7u8DE=Y*Z}3@+Z$SajZcxKuZZ5nRf?O*ZZabgaiwaV;AClad-wlTABApLN|3S+^vlg}+mCi+c$wPuomPmD12V573?zG=HU=p4ceZq?jh5q@}c1|Z8Q1cg@=U5lCYWuTGVtz3-keyH;N$t1tg%-V*pxSeS;gJ@pOl-4hI&JkDMn1RU@*bHi0Ge3jz$k`sWaYETqsB^EZ=59dbeq6CWm860_IHT>YgY;IAsShVCt4eR>U^A+Am|j~{8P9cQ61!x&9_B}sBL!ksV*ep(8sn?fhPQ~WZ1vB2r;S0lgp$^E%9Lk=ro>FEk<j*hf&K74Ty5H``Pg%U^d_B$7FYJNL1DKBJ@}?8i^TN!r=$@F1`n*&%0F+eK_RN6GH)fQmeHM;c}1n;VL9x9as5r>_jK;7aP@cW+@bey=GxtHz%a(K8R)}&gg}(pe-YASvw4eZEaR1r5(J&oPQjLRQG?M({8T5$M(J4ZI_`=-+-x-jUjXU-Qlixn$6YtBH}csW*^-kaTLPt@O+l9r<=2<G1{v}b!^q0oAkKo?*AA`d(Vpn5F3*Ol*&5i@k$n#){~j@GIzO!oladnXGpbA?w7>c6J^f%%e0%CHs>|=4XlX|8O7y{cOP1w!#auTLap)2q~tj-vE`H9w1z|7Znl~mrfvV+5Ri_Hs)dB^xqwpN8AN~<$#z7{JyuvL6o{PVLG;s8tJG0<A1^GE_VK{p-hD7!@(D7+>VhrxEelLiUtnl9t;_miQ6G4a$q01ePAmC`VN^ZHKe}ntT3)8m4U5Nlff}lvS3jFB;o>n2c4C}THODwjat^-nUONMf-r00F`0C#*q8*WfCWmq!1fl|<GDW84Hv`)`kV6p<58gcy(>W+XhA_;>;|;p#=BcTD2VTM$v>|Ez?)2n9yVD-hcrhd%*CyLWq5c=*g*3Z2PV#MyzR710R^FZ>lm_P5nDq`cJ_fdxQs#!ut|W-dB3?txUL)onjishZZ6&Ai)uO#rmtwlASqMMs_VuEAQy%rHP^C8uK2p6hD@88$Xu&~^Y4-c4ei_>Hl@97s$wnmcasjHDM}((~+{D`b$x@b_DbQFO;7`MFA_>m7!phr%LB^~w!tIy*VW3*yKpl%X5N5w%SX;6@131zsa^6N|55`?V>OC|kqZ}I1ym6K_qP(``+lq=cFF7g#G_qT3MeBbmNKgj9V;v=085!b0Ej}Rey*{nrvYc%tUq6?Yi2esWY#S^yj1TTL1$mse*!}VTRl(s0hySKRx!`*Ot!Q-+>%#@XhUTYFO4y0-Nny;tPdgrVYcD<Zzr1LAj+0BdV=Wj-G5G?mIF5op%XXLkT=hXW&YeoTkV&))mC;dJG&?()fcD(|@vZ~u9j<w90G9tt;6_iFk274N<~N|mY`Cd~YmuNIs*;X3yhZ|ii|!Rv46-uZ@0*+_QE7@PLhDC@CFRzm*dgiIZX`Nplppj{9f?X_7+JS6a-OXf&*Ggx$>z}!U;;1fm&8`8cX3P`r)m%A_%I9jV8U8_78AK3WyfHudG$Z+TGIaSS450e8&|mNa#~Z%<&q)D!)Hw@du`)ef11DQy1L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\ No newline at end of file
diff --git a/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/train_seed1337.log b/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/train_seed1337.log
new file mode 100644
index 0000000000..4c3d5dc0d4
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/train_seed1337.log
@@ -0,0 +1,284 @@
+W0401 09:08:58.310000 59103 torch/distributed/run.py:803] 
+W0401 09:08:58.310000 59103 torch/distributed/run.py:803] *****************************************
+W0401 09:08:58.310000 59103 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0401 09:08:58.310000 59103 torch/distributed/run.py:803] *****************************************
+logs/eb2dec5e-981d-4ddb-9e66-eeefa416e585.txt
+val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model
+train_loader:dataset:fineweb10B_sp1024 train_shards:80
+val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632
+model_params:27041372
+XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+world_size:8 grad_accum_steps:1
+sdp_backends:cudnn=False flash=True mem_efficient=False math=False
+attention_mode:gqa num_heads:8 num_kv_heads:4
+tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.03 scalar_lr:0.025
+train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000
+seed:1337
+gptq:reserving 9000ms from training budget, effective=591000ms
+warmup_step:1/20
+warmup_step:2/20
+warmup_step:3/20
+warmup_step:4/20
+warmup_step:5/20
+warmup_step:6/20
+warmup_step:7/20
+warmup_step:8/20
+warmup_step:9/20
+warmup_step:10/20
+warmup_step:11/20
+warmup_step:12/20
+warmup_step:13/20
+warmup_step:14/20
+warmup_step:15/20
+warmup_step:16/20
+warmup_step:17/20
+warmup_step:18/20
+warmup_step:19/20
+warmup_step:20/20
+step:0/20000 val_loss:6.9309 val_bpb:4.1049 train_time:0ms step_avg:0.02ms
+step:1/20000 train_loss:6.9319 train_time:124ms step_avg:123.62ms
+step:2/20000 train_loss:8.9665 train_time:160ms step_avg:79.91ms
+step:3/20000 train_loss:7.6253 train_time:246ms step_avg:82.14ms
+step:4/20000 train_loss:7.0670 train_time:334ms step_avg:83.53ms
+step:5/20000 train_loss:7.0561 train_time:421ms step_avg:84.11ms
+step:6/20000 train_loss:7.1294 train_time:507ms step_avg:84.54ms
+step:7/20000 train_loss:7.0901 train_time:596ms step_avg:85.15ms
+step:8/20000 train_loss:6.7157 train_time:682ms step_avg:85.27ms
+step:9/20000 train_loss:6.3525 train_time:769ms step_avg:85.41ms
+step:10/20000 train_loss:6.0574 train_time:856ms step_avg:85.56ms
+step:500/20000 train_loss:2.3349 train_time:44203ms step_avg:88.41ms
+step:1000/20000 train_loss:2.1886 train_time:88567ms step_avg:88.57ms
+step:1500/20000 train_loss:2.0939 train_time:132937ms step_avg:88.62ms
+step:2000/20000 train_loss:2.0519 train_time:177311ms step_avg:88.66ms
+step:2500/20000 train_loss:2.0059 train_time:221628ms step_avg:88.65ms
+step:3000/20000 train_loss:1.9785 train_time:265951ms step_avg:88.65ms
+step:3500/20000 train_loss:2.0360 train_time:310234ms step_avg:88.64ms
+step:4000/20000 train_loss:2.0635 train_time:354541ms step_avg:88.64ms
+step:4000/20000 val_loss:2.0252 val_bpb:1.1994 train_time:354595ms step_avg:88.65ms
+step:4500/20000 train_loss:1.9822 train_time:398808ms step_avg:88.62ms
+step:5000/20000 train_loss:2.0302 train_time:443078ms step_avg:88.62ms
+step:5500/20000 train_loss:1.9441 train_time:487312ms step_avg:88.60ms
+swa:start step:6000
+step:6000/20000 train_loss:1.9803 train_time:531549ms step_avg:88.59ms
+late_qat:enabled step:6144 scale:0.1498
+step:6500/20000 train_loss:1.9479 train_time:576572ms step_avg:88.70ms
+step:6660/20000 val_loss:1.9103 val_bpb:1.1314 train_time:591082ms step_avg:88.75ms
+stopping_early: wallclock_cap train_time:591082ms step:6660/20000
+peak memory allocated: 23328 MiB reserved: 23378 MiB
+ema:applying EMA weights
+DIAGNOSTIC post_ema val_loss:1.9086 val_bpb:1.1304 eval_time:2089ms
+Serialized model: 106240695 bytes
+Code size: 71382 bytes
+gptq:calibrating with 64 batches (training data)...
+gptq:calibrated 66 layers in 6.8s
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+Serialized model int6+brotli: 15724136 bytes
+Total submission size int6+brotli: 15795518 bytes
+final_int6_roundtrip val_loss:1.9139 val_bpb:1.1335 eval_time:6765ms
+final_int6_roundtrip_exact val_loss:1.91393962 val_bpb:1.13354285
+final_int6_sliding_window val_loss:1.8601 val_bpb:1.1017 stride:64 eval_time:165229ms
+final_int6_sliding_window_exact val_loss:1.86009741 val_bpb:1.10165738
+final_int8_zlib_roundtrip_exact val_loss:1.86009741 val_bpb:1.10165738
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+ttt_sliding:done val_loss=1.874319 val_bpb=1.110080 elapsed=458.2s
+legal_ttt val_loss:1.8743 val_bpb:1.1101 eval_time:458753ms
+legal_ttt_exact val_loss:1.87431916 val_bpb:1.11008032
diff --git a/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/train_seed2024.log b/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/train_seed2024.log
new file mode 100644
index 0000000000..653b0fa29f
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/train_seed2024.log
@@ -0,0 +1,284 @@
+W0401 10:02:43.624000 58977 torch/distributed/run.py:803] 
+W0401 10:02:43.624000 58977 torch/distributed/run.py:803] *****************************************
+W0401 10:02:43.624000 58977 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0401 10:02:43.624000 58977 torch/distributed/run.py:803] *****************************************
+logs/d2af47e0-6e74-4047-9eff-263e5baad552.txt
+val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model
+train_loader:dataset:fineweb10B_sp1024 train_shards:80
+val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632
+model_params:27041372
+XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+world_size:8 grad_accum_steps:1
+sdp_backends:cudnn=False flash=True mem_efficient=False math=False
+attention_mode:gqa num_heads:8 num_kv_heads:4
+tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.03 scalar_lr:0.025
+train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000
+seed:2024
+gptq:reserving 9000ms from training budget, effective=591000ms
+warmup_step:1/20
+warmup_step:2/20
+warmup_step:3/20
+warmup_step:4/20
+warmup_step:5/20
+warmup_step:6/20
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+warmup_step:8/20
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+warmup_step:15/20
+warmup_step:16/20
+warmup_step:17/20
+warmup_step:18/20
+warmup_step:19/20
+warmup_step:20/20
+step:0/20000 val_loss:6.9297 val_bpb:4.1041 train_time:0ms step_avg:0.01ms
+step:1/20000 train_loss:6.9302 train_time:124ms step_avg:123.55ms
+step:2/20000 train_loss:8.8536 train_time:160ms step_avg:80.07ms
+step:3/20000 train_loss:7.6068 train_time:247ms step_avg:82.20ms
+step:4/20000 train_loss:7.2173 train_time:333ms step_avg:83.37ms
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+step:6/20000 train_loss:7.0735 train_time:507ms step_avg:84.50ms
+step:7/20000 train_loss:7.0091 train_time:595ms step_avg:85.03ms
+step:8/20000 train_loss:6.7258 train_time:682ms step_avg:85.28ms
+step:9/20000 train_loss:6.3312 train_time:769ms step_avg:85.46ms
+step:10/20000 train_loss:6.0085 train_time:856ms step_avg:85.58ms
+step:500/20000 train_loss:2.3368 train_time:44236ms step_avg:88.47ms
+step:1000/20000 train_loss:2.1920 train_time:88775ms step_avg:88.77ms
+step:1500/20000 train_loss:2.0955 train_time:133248ms step_avg:88.83ms
+step:2000/20000 train_loss:2.0519 train_time:177670ms step_avg:88.83ms
+step:2500/20000 train_loss:2.0075 train_time:222042ms step_avg:88.82ms
+step:3000/20000 train_loss:1.9797 train_time:266371ms step_avg:88.79ms
+step:3500/20000 train_loss:2.0357 train_time:310738ms step_avg:88.78ms
+step:4000/20000 train_loss:2.0657 train_time:355097ms step_avg:88.77ms
+step:4000/20000 val_loss:2.0266 val_bpb:1.2003 train_time:355149ms step_avg:88.79ms
+step:4500/20000 train_loss:1.9832 train_time:399484ms step_avg:88.77ms
+step:5000/20000 train_loss:2.0312 train_time:443726ms step_avg:88.75ms
+step:5500/20000 train_loss:1.9457 train_time:487981ms step_avg:88.72ms
+swa:start step:6000
+step:6000/20000 train_loss:1.9829 train_time:532213ms step_avg:88.70ms
+late_qat:enabled step:6136 scale:0.1498
+step:6500/20000 train_loss:1.9483 train_time:577234ms step_avg:88.81ms
+step:6653/20000 val_loss:1.9119 val_bpb:1.1323 train_time:591133ms step_avg:88.85ms
+stopping_early: wallclock_cap train_time:591133ms step:6653/20000
+peak memory allocated: 23328 MiB reserved: 23378 MiB
+ema:applying EMA weights
+DIAGNOSTIC post_ema val_loss:1.9102 val_bpb:1.1313 eval_time:2091ms
+Serialized model: 106240695 bytes
+Code size: 71382 bytes
+gptq:calibrating with 64 batches (training data)...
+gptq:calibrated 66 layers in 6.8s
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+Serialized model int6+brotli: 15725397 bytes
+Total submission size int6+brotli: 15796779 bytes
+final_int6_roundtrip val_loss:1.9155 val_bpb:1.1345 eval_time:6722ms
+final_int6_roundtrip_exact val_loss:1.91553651 val_bpb:1.13448862
+final_int6_sliding_window val_loss:1.8619 val_bpb:1.1027 stride:64 eval_time:166619ms
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+legal_ttt_exact val_loss:1.87599873 val_bpb:1.11107506
diff --git a/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/train_seed42.log b/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/train_seed42.log
new file mode 100644
index 0000000000..eb915eab53
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-01_MuonEqR_ContextSLOT_QKGain5/train_seed42.log
@@ -0,0 +1,284 @@
+W0401 09:38:04.472000 1004 torch/distributed/run.py:803] 
+W0401 09:38:04.472000 1004 torch/distributed/run.py:803] *****************************************
+W0401 09:38:04.472000 1004 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0401 09:38:04.472000 1004 torch/distributed/run.py:803] *****************************************
+logs/d6969175-9444-464f-af67-5c5cca643361.txt
+val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model
+train_loader:dataset:fineweb10B_sp1024 train_shards:80
+val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632
+model_params:27041372
+XSA:last_11 active_layers:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+world_size:8 grad_accum_steps:1
+sdp_backends:cudnn=False flash=True mem_efficient=False math=False
+attention_mode:gqa num_heads:8 num_kv_heads:4
+tie_embeddings:True embed_lr:0.035 head_lr:0.0 matrix_lr:0.03 scalar_lr:0.025
+train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000
+seed:42
+gptq:reserving 9000ms from training budget, effective=591000ms
+warmup_step:1/20
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+step:2/20000 train_loss:8.7955 train_time:163ms step_avg:81.32ms
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+step:500/20000 train_loss:2.3265 train_time:44143ms step_avg:88.29ms
+step:1000/20000 train_loss:2.1908 train_time:88540ms step_avg:88.54ms
+step:1500/20000 train_loss:2.0966 train_time:133006ms step_avg:88.67ms
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+step:2500/20000 train_loss:2.0071 train_time:221890ms step_avg:88.76ms
+step:3000/20000 train_loss:1.9824 train_time:266289ms step_avg:88.76ms
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+step:4000/20000 train_loss:2.0618 train_time:355036ms step_avg:88.76ms
+step:4000/20000 val_loss:2.0277 val_bpb:1.2009 train_time:355087ms step_avg:88.77ms
+step:4500/20000 train_loss:1.9845 train_time:399405ms step_avg:88.76ms
+step:5000/20000 train_loss:2.0320 train_time:443733ms step_avg:88.75ms
+step:5500/20000 train_loss:1.9466 train_time:488050ms step_avg:88.74ms
+swa:start step:6000
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+late_qat:enabled step:6133 scale:0.1500
+step:6500/20000 train_loss:1.9513 train_time:577541ms step_avg:88.85ms
+step:6650/20000 val_loss:1.9135 val_bpb:1.1333 train_time:591207ms step_avg:88.90ms
+stopping_early: wallclock_cap train_time:591207ms step:6650/20000
+peak memory allocated: 23338 MiB reserved: 23458 MiB
+ema:applying EMA weights
+DIAGNOSTIC post_ema val_loss:1.9119 val_bpb:1.1323 eval_time:2096ms
+Serialized model: 106240695 bytes
+Code size: 71382 bytes
+gptq:calibrating with 64 batches (training data)...
+gptq:calibrated 66 layers in 6.8s
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+gptq_quantize: 66 GPTQ layers, 0 naive layers
+Serialized model int6+brotli: 15721781 bytes
+Total submission size int6+brotli: 15793163 bytes
+final_int6_roundtrip val_loss:1.9172 val_bpb:1.1354 eval_time:21288ms
+final_int6_roundtrip_exact val_loss:1.91715507 val_bpb:1.13544722
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