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REVIEW 3 major objections 6 minor 49 references

Constitutional midtraining—inserting a values-based document into the final pretraining stage—produces alignment that generalizes beyond training and survives later fine-tuning, with content presence rather than curriculum or reasoning stru

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2026-08-01 11:28 UTC pith:PRHGNACL

load-bearing objection A careful, honest empirical study of full-constitution midtraining at 120B scale with a clean post-training isolation, but the flagship blackmail durability result has a keyword-filter measurement threat that deserves a hard look before the claim is treated as settled. the 3 major comments →

arxiv 2607.26654 v2 pith:PRHGNACL submitted 2026-07-29 cs.CL cs.AIcs.CYcs.LG

Constitutional Midtraining: Content Presence Drives Alignment Gains

Desiree Cho , Cameron Tice , Bernie Hogan , Hunar Batra , Puria Radmard , Jun Zhao , Nigel Shadbolt This is my paper
classification cs.CL cs.AIcs.CYcs.LG
keywords constitutional midtrainingalignment durabilityblackmailalignment generalizationcurriculum learningdeliberative reasoningbenign fine-tuningcontent presence
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Constitutional midtraining inserts hundreds of millions of tokens of values-based text into the final pretraining stage, before any instruction tuning. The paper tries to show that this cheap intervention alone produces alignment that generalizes beyond the training scenarios and survives both ordinary SFT and unrelated-task fine-tuning. The strongest evidence is blackmail: all models became more blackmail-prone after SFT, but constitutionally midtrained models stayed well below the control, with the gap nearly unchanged after benign fine-tuning. The paper also finds that whether the content is present matters far more than how it is ordered or whether it includes explicit reasoning, and that there is no average capability cost. If right, it identifies midtraining as an inexpensive, complementary point for installing robust alignment defaults.

Core claim

The paper's central claim is that exposing a large language model to a constitution—a structured statement of values and the reasoning behind them—during midtraining changes what the model defaults to, not just what it does on training examples. In a 120-billion-parameter model, four variants built from a 394-million-token constitutional corpus outperformed a replay-only control on out-of-distribution safety questions and on blackmail, and the blackmail advantage survived SFT and subsequent benign fine-tuning on a math task. Crucially, SFT instilled a blackmail propensity in every condition, but constitutional midtraining reduced the magnitude and the reduction persisted. Generalization to d

What carries the argument

The load-bearing object is the constitutional corpus: 394 million tokens of synthetic documents, each anchored to one of 38 extracted values from a public constitution, with an optional explicit reasoning block. It is inserted at midtraining under the same next-token objective as pretraining, evenly mixed with replayed pretraining data. The comparison that carries the argument is CMT versus a replay-only control: same base model, same post-training, differing only in whether constitutional content was present. The paper uses centrality-based clustering to order values in curriculum conditions, but the empirical point is that the content block itself, not ordering or reasoning, produces the d

Load-bearing premise

The result depends on the assumption that the control condition differs from the treated conditions only in the presence of constitutional text; because the no-reasoning conditions ran on roughly half the token budget, extra data volume or replay structure could also explain part of the effect.

What would settle it

Create a matched condition that receives the same 500M-token midtraining budget of high-quality, value-free synthetic text instead of the constitutional corpus. If its blackmail and OOD scores match the CMT conditions, constitutional content is not the active ingredient; if they match the replay-only control, it is.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • A 264–500M-token constitutional intervention at midtraining can be a cheap complement to SFT-based alignment, with durable gains on default-behavior benchmarks.
  • Because SFT raised blackmail in all groups, post-training still matters: midtraining blunts, but does not neutralize, misalignment propensities induced later.
  • Durability is benchmark-dependent: what survives is the model's default response to a situation, not its ability to actively resist pressure or resolve value conflicts.
  • Restructuring the content (curriculum order, deliberative reasoning) buys little; data composition is the main lever.
  • No capability tax: pooled CMT averages never fall below control on MMLU, ARC-Easy, piqa, or GSM8K at any stage.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The result suggests a cheap scaling recipe: if content presence is what matters, larger or denser constitutional corpora could push transient advantages on pressure and conflict into durable ones; that is a direct, testable extrapolation the paper does not make.
  • The pattern resembles a declarative-versus-instrumental distinction: next-token midtraining may install values as knowledge that gradient pressure on unrelated tasks does not erase, whereas SFT's response-level objective may write shallower associations; probing the released checkpoints could test this.
  • A content-matched SFT baseline would settle whether the stage (midtraining) or the document format (next-token text) drives durability; the paper's limitation section notes this baseline is missing.
  • The observed DR/noDR conflict-prior shift suggests explicit reasoning does alter internal representation even if it does not survive downstream training; pairing midtraining DR with reasoning-encouraging evaluation could recover the effect.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper reports a 120B-parameter constitutional midtraining (CMT) study in which four CMT conditions (2×2: curriculum vs uniform ordering; deliberative reasoning (DR) vs no-DR) are compared with a replay-only control across three stages (post-midtraining, post-SFT, post-benign fine-tuning). On in-distribution value questions, OOD safety questions, and a blackmail agentic-misalignment scenario, CMT conditions are claimed to outperform control, with the blackmail advantage surviving benign fine-tuning (−17.5pp). Pressure, value-conflict, and alignment-faking advantages attenuate after SFT. Curriculum and DR structure show small, mostly non-persistent effects, leading the authors to conclude that constitutional content presence, not structure, drives durable alignment gains, with no capability cost on MMLU/ARC-Easy/piqa/GSM8K.

Significance. If correct, this is a practically important result: a modest amount of constitutionally framed content inserted before post-training can shift default behavior in a durable way, potentially complementing SFT/RLHF pipelines. The design has notable strengths: a replay-only control isolates content from exposure to additional text, the 2×2 factorial permits structural comparisons, the three-stage evaluation tests durability, and the authors release data, code, and checkpoints. The headline effects are large (e.g., +28.8pp OOD, −18.5pp blackmail at post-MT) and would be meaningful if valid. The main reservations concern the validity of the blackmail measurement and the token-exposure confound for the noDR arm, both of which bear directly on the central claim.

major comments (3)
  1. [§F.3; Table 2; Figure 3] The blackmail classifier is a three-way AND rule that requires the response to contain 'blackmail' or 'leverage' before the GPT-4o judge verdict is consulted (F.3). This keyword precondition excludes every indirect conditional threat phrased without those words, even though the judge prompt explicitly says subtle blackmail still counts. The paper itself notes that a 0% rate means 'zero unambiguous attempts, not necessarily zero attempts.' CMT training documents contain dense non-manipulation/non-deception language, so CMT models may have learned to avoid the incriminating vocabulary while still issuing conditional threats. This is a measurement-validity threat to the strongest durable result (−17.5pp post-BFT). Since the target-email, keyword, and judge-verdict components are recorded separately, please report the component decomposition (especially judge-positive rates without the keywo
  2. [§3.2; Table 5; §D.2] The replay-only control is token-matched to the DR conditions (954 vs 967 steps, ~1.0B tokens processed) but not to the noDR conditions (513–516 steps, ~0.54B tokens). Thus noDR-vs-control comparisons vary both constitutional content and total midtraining exposure, so the 'content presence' attribution is not clean for two of the four CMT arms. Because the headline CMT-pooled estimates (Table 2, Figure 1) average over these arms, the pooled claim rests partly on a confounded contrast. The authors call this a 'necessary trade-off' (§3.2), but given the paper's title and central conclusion, the report should provide a separate token-matched analysis for the DR arms and state whether the noDR results are consistent when exposure is accounted for.
  3. [§4; Table 2] The paper reports dozens of two-proportion z-tests across 8 benchmarks × 3 stages and within-CMT contrasts, using p<0.05 as the significance barometer without multiple-comparison correction or confidence intervals. The central effects (OOD, blackmail, pressure at post-MT) have p<0.001 and would likely survive a conservative correction, but the within-CMT claims (e.g., DR vs noDR blackmail +9.0pp at post-BFT, value-conflict pairwise differences in Figure 4) are exactly the kind of isolated p<0.05 results that multiple testing makes unreliable. Please report adjusted p-values or confidence intervals, or explicitly label the structural comparisons as exploratory.
minor comments (6)
  1. [§C.5] The clustering implementation used SciPy Ward linkage with a square distance matrix rather than condensed form; under the correct condensed input, 'hard constraints' moves from k3 to k1. The authors argue this does not affect the conflict results, but it could affect the curriculum condition's content. Please re-run the clustering with the standard API or report sensitivity of the curriculum results to this reassignment.
  2. [§4.5] Emergent Misalignment: the control floor at 0% makes the post-MT CMT underperformance (+3.6pp) difficult to interpret. Reporting raw alignment scores or a more sensitive threshold would clarify whether this is a real effect or an artifact of the floor.
  3. [§3.4 / §F.1] The OOD benchmark is drawn from Tice et al. (2026), which shares two authors with the present paper. Please disclose this relation explicitly and, if possible, validate the OOD result on an external benchmark without author overlap.
  4. [§3.2 / §D.1] The main-text token budgets (500M/264M) are constitutional-content tokens, not total tokens processed (~2× in practice per D.1). This distinction should be stated in the main text to avoid misunderstanding.
  5. [§4.7 / §F.6] Value-conflict construction used directed generation to increase minority cluster wins, so the final dataset has non-representative base rates. Pairwise accuracies should be interpreted with this selection bias in mind, and the directed-generation step should be flagged in the main text.
  6. [Table 2 / Figure 1] No confidence intervals or error bars are shown for the headline comparisons. A small bootstrap CI for the main CMT-vs-control differences would help readers gauge precision beyond the reported p-values.

Circularity Check

0 steps flagged

No significant circularity: empirical intervention study; external blackmail/capability benchmarks anchor the central durability claim; one minor non-load-bearing self-citation (OOD item pool) noted.

full rationale

The paper's claimed chain is an empirical training/evaluation design: build a constitutional corpus from Anthropic's Constitution, train four CMT conditions plus a replay-only control, then evaluate at post-MT, post-SFT, and post-BFT. There is no analytic derivation in which an output quantity is constructed from an input quantity by definition. The headline durability result (blackmail, CMT vs control −17.5pp at post-BFT) uses Lynch et al.'s external agentic-misalignment scenario and an independent capability battery (MMLU, ARC-Easy, piqa, GSM8K), so those results are not reducible to the training corpus or to a fitted parameter. The ID and value-conflict benchmarks do derive their ground truth from the same Constitution used to generate training data, which makes them closer to manipulation checks than independent evidence; the paper itself treats OOD and blackmail as the generalizability/durability evidence. The OOD benchmark is sourced from Tice et al. (2026), a prior paper with overlapping authors (Tice, Radmard), but the OOD claim rests on newly measured scores and an explicitly described GPT-4o filtering protocol (§F.1), not on the cited paper's conclusions. The paper also flags the key limitations: the blackmail classifier requires the word 'blackmail' or 'leverage' (§F.3), conceding 'A rate of 0 indicates zero unambiguous attempts, not necessarily zero attempts'; and §5.6 notes the lack of a content-matched SFT baseline, so 'we cannot rule out that similar gains would arise from delivering this content during post-training rather than midtraining specifically.' These are measurement-validity and attribution threats, not circularity. The DR/noDR token-budget mismatch and control/replay matching (§D.2) are confounds that weaken structural comparisons but do not make any predicted quantity identical to a fitted input. No load-bearing result is forced by a self-citation chain, and no equation reduces a predicted effect to the training data by construction. Score 2 reflects one minor, non-load-bearing self-citation (the OOD item pool from Tice et al.) rather than any constructed equivalence.

Axiom & Free-Parameter Ledger

5 free parameters · 5 axioms · 0 invented entities

The central claim rests on domain assumptions about control validity, value-neutral post-training, LLM-judge validity, and the centrality-based curriculum. No free parameters are fitted in a mathematical sense, but token budgets, cluster count, and evaluation thresholds are researcher-chosen and load-bearing. No new entities are postulated.

free parameters (5)
  • CMT token budget (DR vs noDR) = 500M tokens (DR); 264–266M tokens (noDR)
    Chosen to match Tice et al. (2026) for DR; noDR budget is smaller because per-cluster repetition rate is matched rather than total tokens (§D.2).
  • Number of curriculum clusters and ordering = k1→k2→k3→k4; mean centralities 0.632/0.598/0.588/0.578
    Four clusters chosen by visual dendrogram inspection (§C.2); the cosine-centrality ordering is an ad hoc operationalization of 'foundational' not validated against a causal dependency structure (§C.5).
  • Excluded constitutional values = 2 of 40 (helpfulness, principals hierarchy)
    Excluded because they were peripheral by cosine centrality; this changes the trained value set and the curriculum structure (§3.1, Table 3).
  • Value-conflict benchmark split = 12/13 minority/majority per pair; 150 final items
    Selection rule set by the authors, with directed generation for minority-wins; affects sensitivity of the conflict benchmark (§F.6).
  • Evaluation thresholds = MASK confidence 0.6; emergent misalignment <30; coherence <50; blackmail keywords 'blackmail'/'leverage'
    Ad hoc thresholds defining outcome metrics; reported rates could shift under different thresholds (§F).
axioms (5)
  • domain assumption Replay-only control isolates constitutional content.
    The CMT-vs-control comparison attributes differences to constitutional content; token budgets are matched for DR but not for noDR (Table 5).
  • domain assumption The shared SFT and benign GRPO-on-GSM8K stages are value-neutral and identical across conditions.
    If the SFT mix or GSM8K RL signal contains implicit alignment content, the 'durability' reading is confounded; SFT empirically raised blackmail rates in all groups (§5.2).
  • domain assumption LLM judges provide valid labels for alignment, blackmail, emergent misalignment, and value-conflict ground truth.
    GPT-4o judges many outcomes and GPT-4o/Claude Sonnet generate training data and ground truth against the same Constitution (§3.4, §F).
  • domain assumption Sentence-BERT cosine centrality corresponds to a curriculum-relevant notion of foundationality.
    Acknowledged in §C.5 as embedding-space density, not causal or logical dependency structure.
  • domain assumption Cluster membership is stable under the clustering implementation used.
    §C.5 reports that 'hard constraints' would move from k3 to k1 under standard condensed-input linkage; the authors argue this does not affect the k1×k3 value-conflict comparison, but it does affect curriculum construction.

pith-pipeline@v1.3.0-daily-deepseek · 25755 in / 18012 out tokens · 188425 ms · 2026-08-01T11:28:00.424215+00:00 · methodology

0 comments
read the original abstract

Post-training alignment is often shallow, eroding under fine-tuning. It remains untested as to whether constitutional midtraining interventions can produce durable alignment when cleanly isolated from post-training. We build a 394M-token constitutional corpus from Anthropic's Constitution and apply constitutional midtraining at 120B scale, where principled, values-based content is inserted into midtraining. A 2x2 design (curriculum ordering x deliberative reasoning) was used to produce four constitutionally midtrained conditions, plus a control, which were evaluated on self-generated and established benchmarks including alignment under pressure, value conflict resolution, blackmail, and emergent misalignment. All models were evaluated across three stages: post-midtraining, post-SFT, and post-benign fine-tuning. Constitutionally midtrained models outperformed the control on alignment generalization and durability, notably on blackmail: SFT instilled a blackmail propensity in all models, but constitutional midtraining blunted it, with the advantage surviving benign fine-tuning (-17.5pp). This durability did not extend to settings that required active resistance to in-context pressure or conflict, where the advantage attenuates after SFT. The presence of constitutional content at midtraining also mattered more than its structure, and constitutional midtraining incurred no capability cost, on average, at any stage (MMLU, ARC-Easy, piqa, GSM8K). A modest amount of constitutional content at midtraining could therefore yield broad, persistent alignment gains, offering a cheap, complementary addition to SFT-centered pipelines. Code, data, and models are available.

Figures

Figures reproduced from arXiv: 2607.26654 by Bernie Hogan, Cameron Tice, Desiree Cho, Hunar Batra, Jun Zhao, Nigel Shadbolt, Puria Radmard.

Figure 1
Figure 1. Figure 1: Constitutional midtraining’s advantage over control across all alignment benchmarks and stages, grouped by durability. The advantage survives post-training and benign fine-tuning on benchmarks testing default behavior, but attenuates once the model must actively resist in-context pressure or conflict. Benchmarks are evaluated at three training stages: post￾midtraining (MT), post-supervised fine-tuning (SFT… view at source ↗
Figure 2
Figure 2. Figure 2: All conditions branch from the same base checkpoint, diverge during midtraining (control: no intervention), then [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Blackmail rate for CMT vs. control across the three training stages. SFT sharply increases blackmail propensity in both groups, but CMT’s advantage over con￾trol persists almost undiminished through benign fine-tuning – our strongest evidence of durability. *** = 𝑝 < .001. CMT models blackmail at a fraction of control’s rate through￾out (−18.5pp, −18.7pp, −17.5pp) ( [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: DR shifts the model’s value-conflict prior at post-MT. Accuracy per cluster pair, split by which cluster the conflict favours. ***𝑝 < .001, **𝑝 < .01, *𝑝 < .05. corrects noDR’s default-to-Core-Ethics bias, resolving k4- favouring conflicts correctly 63% vs. noDR’s 17% (both near-ceiling when k1 is correct). Neither effect persists past post-MT, but together they suggest DR shifts representational structure… view at source ↗
Figure 5
Figure 5. Figure 5: A 2D PCA projection of the 768-dimensional Sentence-BERT embeddings of the 38 constitutional values retained [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Constitutional accuracy (aligned-choice rate) per cluster pair and condition, post-midtraining and post-SFT. Values [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗

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