mse_validation/notes/mse-validation-debug-process-2026-05-05.md

MSE Validation Debug Process - 2026-05-05

Summary

This session debugged why distributional MSE validation initially failed for dense agentic workloads, especially SWE-bench multi-turn. The final answer is that token-count matching alone is not enough. Faithful synthetic replay needs three properties:

1. model-tokenizer accounting,
2. code-agent morphology matching,
3. APC-aware shared-prefix structure.

After adding morphology-aware filler and a 1024-token shared synthetic prefix, the SWE-bench C=5 source-locked validation closed the deep-turn E2EL gap:

Condition	Turn 10-19 E2EL delta
APC on, no shared prefix	+31.1%
APC off	+11.7%
APC on, shared prefix	-3.2%

This supports the paper framing that distributional replay must model the generative structure of agent sessions, not only marginal token lengths.

Data Location

Canonical archived data is in R2:

s3://agent-bench/bench_mse/
  morphology-experiment-2026-05-05.md
  scripts/
  src/
  h100/results/
  h100-2/results/

R2 endpoint:

https://b33fe7347f25479b27ec9680eff19b78.r2.cloudflarestorage.com

Bucket and prefix:

Bucket: agent-bench
Prefix: bench_mse/

Local pulled result roots used during this session:

inference-benchmark/results/mse_validation_paper/
inference-benchmark/results/mse_validation_option2/
inference-benchmark/results/mse_validation_source_locked/
inference-benchmark/results/mse_validation_source_locked_pair/
inference-benchmark/results/mse_validation_morphology/
inference-benchmark/results/mse_validation_highc/
inference-benchmark/results/mse_validation_apc_off/
inference-benchmark/results/mse_validation_prefix_aware/

Initial Failure

The first MSE validation runs looked unusable for SWE-bench and TerminalBench. Errors reached 100-1000% for dense code/terminal workloads even when OSWorld looked reasonable.

The first root cause was token estimation. The old synthetic filler used labels like s0_t0_user_0, then estimated tokens using a natural-language TOKEN_WORD_RATIO. Those labels tokenize into many subword pieces because of underscores and digits. The generator produced far more real model tokens than intended.

Fix:

• rebuild distributions using captured vLLM input tokens or the actual tokenizer/chat template,
• remove the old label-token heuristic,
• use calibrated synthetic text measured by the model tokenizer.

This made short H100 paper runs plausible:

Workload	C	Metric outcome
SWE-bench	5	TPOT +3%, E2EL +3%, TTFT +28%
TerminalBench	5	TPOT +4%, E2EL +5%, TTFT +27%
SWE-bench	20	TPOT -1%, E2EL +4%, TTFT +15%

TTFT remained noisy; TPOT and E2EL were the main validation metrics.

Sampling Fixes

The next issue was population variance. Distributional sampling could duplicate source sessions and skip others, which inflated per-turn error when comparing to real trace replay.

Implemented and tested:

• no-replacement source-session sampling,
• source-locked replay through SOURCE_SESSION_IDS_FILE,
• source-ID metadata in request outputs.

This established two modes:

Mode	Purpose
No-replacement	honest generator behavior for paper validation
Source-locked	diagnostic ablation to isolate synthetic-content error

Source-locking confirmed the SWE diagnosis was not caused by session mismatch. The overlap was 40/40 source sessions, Jaccard 1.000.

SWE-Bench Residual Problem

Even after tokenizer accounting and source-locking, SWE-bench had a deep-turn residual gap. Shallow turns were nearly perfect, but turns 10-19 were off.

Source-locked English filler, C=5:

Turn bin	E2EL delta
00-04	+3.2%
05-09	+2.2%
10-19	+45.5%
20-29	+15.8%

This localized the issue to deeper agent context, not the whole workload.

Morphology Experiment

The next hypothesis was text morphology. Synthetic English filler had about 2.2x more characters per token than real SWE-bench code/tool-output text.

Measured chars/token:

Condition	Chars/token
English MSE	8.39
Real SWE trace	3.80
Code-morph MSE	3.68

The code-morph generator hit the chars/token target, but did not fully close the latency gap.

Turn bin	English E2EL delta	Code-morph E2EL delta
00-04	+3.2%	+1.9%
05-09	+2.2%	+1.0%
10-19	+45.5%	+31.1%
20-29	+15.8%	+12.4%

Conclusion: morphology was a real factor, but only a partial factor. Token count and chars/token parity were insufficient for deep SWE-bench replay.

Paper-safe wording from this stage:

Token-count matching alone is sufficient for TerminalBench and shallow SWE turns, but not for deep SWE-bench traces. Deep code-agent workloads require morphology-aware synthetic replay; otherwise request preprocessing and prefix handling differ even when token counts match.

High-Concurrency Check

C=100 runs were launched to see whether higher queue pressure made the mismatch worse.

Result summary:

Condition	TTFT p50 MSE/REAL	TPOT p50 MSE/REAL	E2EL p50 MSE/REAL	Turn 10-19 E2EL delta
TerminalBench English	2966 / 1825 ms	136 / 60 ms	7951 / 3964 ms	+29.4%
SWE code-morph	8349 / 6140 ms	204 / 181 ms	14515 / 12248 ms	+16.3%
SWE English	7591 / 6276 ms	206 / 177 ms	13597 / 12347 ms	+9.5%

Interpretation:

• C=100 introduces broad saturation artifacts.
• Higher concurrency does not cleanly isolate the SWE-specific bug.
• C=5 source-locked runs remain the cleanest diagnostic setting.

APC-Off Ablation

The key discriminating test was to turn off vLLM automatic prefix caching.

Sanity check:

Condition	turn1/turn0 TTFT ratio
APC on	0.67x
APC off	1.40x

The ratio confirmed APC was behaviorally disabled.

APC-off result:

Turn bin	APC on, no shared prefix	APC off
00-04	+1.9%	+28.6%
05-09	+1.0%	+16.9%
10-19	+31.1%	+11.7%
20-29	+6.9%	+0.1%

Aggregate:

Metric	APC on, no shared prefix	APC off
TTFT	+23.8%	+4.2%
TPOT	+14.7%	+10.6%
E2EL	+21.6%	+9.3%

Conclusion: APC was amplifying the residual synthetic-vs-real mismatch. The issue was no longer simple token-count error.

vLLM APC Check

The vLLM APC documentation confirms that automatic prefix caching reuses KV cache for new requests that share an exact token prefix with previous requests. APC is token-prefix based, not session-ID based.

Implications:

• Within-session growing transcripts are prefix-eligible if the rendered token prefix is exact.
• Cross-session shared harness/system prefixes are also prefix-eligible.
• Full cache behavior still depends on token-block hashing, exact chat-template rendering, eviction, and scheduler pressure.

Important caveat: runner metadata such as cached_context_tokens is a logical estimate from previous prompt length. It is not server-side APC telemetry. The ablations prove serving behavior changes under APC; they do not directly log vLLM internal cache-hit counters.

Prefix-Aware Generator Fix

The final fix was to add a shared synthetic prefix across distributional sessions.

Implementation:

• DISTRIBUTIONAL_PREFIX_AWARE=1
• DISTRIBUTIONAL_SHARED_PREFIX_TOKENS=1024
• DISTRIBUTIONAL_PREFIX_BLOCK_SIZE=16
• PREFIX_AWARE_SYNTHETIC=on in scripts/run_mse_validation.sh

The shared prefix is inserted as a system message in MSE/distributional requests only. It is subtracted from the first-turn synthetic user payload so sampled total-context token targets remain matched.

Metadata in the pulled prefix-aware JSON confirms:

Field	Value
prefix_aware_synthetic	true
shared_prefix_requested_tokens	1024
shared_prefix_target_tokens	1024
shared_prefix_actual_tokens	1024
shared_prefix_block_size	16
shared_prefix_block_aligned	true
synthetic_filler_style	code
synthetic_target_chars_per_token	3.8

Final Prefix-Aware Result

Request-level median recomputation from the pulled local JSONs:

Turn bin	No shared prefix	APC off	Shared prefix
00-04	+1.9%	+28.6%	-0.4%
05-09	+1.0%	+16.9%	-2.0%
10-19	+31.1%	+11.7%	-3.2%
20-29	+6.9%	+0.1%	+1.7%

The launcher handoff reported +5.3% for the shared-prefix 20-29 bin under its aggregation path; both computations keep the late bin inside the noise-scale band. The key diagnostic bin, turns 10-19, is stable at -3.2%.

Aggregate median deltas:

Metric	No shared prefix	APC off	Shared prefix
TTFT	+23.8%	+4.2%	-6.1%
TPOT	+14.7%	+10.6%	+2.9%
E2EL	+21.6%	+9.3%	-2.4%

Final Interpretation

The best-supported conclusion is:

Distributional replay for agentic serving must match both workload token statistics and the serving-visible prefix structure. Token-count-only replay fails for deep SWE-bench turns because it misses code morphology and shared-prefix APC structure. With morphology-aware filler and a shared APC-aligned harness prefix, distributional replay matches real trace replay within measurement noise across turn-depth bins.

Avoid overclaiming:

• Do not say we directly measured vLLM internal APC hit rates unless server telemetry is added.
• Do not say token count alone is sufficient.
• Do not frame the shared prefix as a paper trick; frame it as modeling the real agent harness/system prompt prefix that real sessions share.

Strong paper framing:

Synthetic replay must preserve the generative process of agent sessions. For code-agent workloads, this means matching token lengths, code-like text morphology, and shared harness prefixes that serving systems cache. Our source-locked H100 ablation shows that adding APC-aware shared-prefix structure closes the deep-turn SWE-bench E2EL gap from +31.1% to -3.2%.

Paper Table Candidate

Use this compact table for the methods or validation section:

Replay variant	Morphology-aware	Shared prefix	APC	Aggregate E2EL delta	Turn 10-19 E2EL delta
Code-morph baseline	yes	no	on	+21.6%	+31.1%
Code-morph, APC off	yes	no	off	+9.3%	+11.7%
Prefix-aware replay	yes	yes, 1024 tokens	on	-2.4%	-3.2%

Caption:

Source-locked SWE-bench C=5 validation on H100/Llama-3.1-8B/vLLM. Distributional and real-trace replay use the same 40 source sessions. Errors are median synthetic-vs-real deltas.

Code Touchpoints

Local implementation files:

• inference-benchmark/src/workloads/distributional.py
• inference-benchmark/scripts/run_mse_validation.sh
• inference-benchmark/tests/test_distributional_workloads.py

Relevant local docs:

• docs/morphology-experiment-2026-05-05.md
• docs/mse-validation-sampling-plan.md
• docs/mse-validation-results.md

Validation run locally after the prefix-aware implementation:

python3 -m unittest tests.test_distributional_workloads
bash -n scripts/run_mse_validation.sh
python3 -m py_compile inference-benchmark/src/workloads/distributional.py

Next Work

1. Add a small plotting script/table extractor for the final paper figure.
2. If time permits, add vLLM server-side APC telemetry to confirm actual cache-hit counters.
3. Update the NeurIPS methodology text to describe APC-aware distributional replay.
4. Keep the negative result: token-count-only replay is insufficient for deep code-agent workloads.