Thinking in a Low-Resource Language: What SFT Builds, What RL Fixes, What Accuracy Cannot See

Ayoub Kirouane; Christos Petrocheilos

lab	base model	total	active	routing	fine-tuned release
Alibaba	Qwen3.6-35B-A3B	36.0B	3.97B	8 of 256	Sophea-Qwen3.6-v1
OpenAI	Gpt-OSS-20B	20.9B	3.60B	4 of 32	Sophea-OSS-v1
NVIDIA	NemotronH-30B-A3B	31.6B	3.58B	6 of 128	Sophea-Nemo-3-Nano-v1
NVIDIA	Nemotron-3.5-Lightning-30B-A3B	31.6B	3.58B	6 of 128	Sophea-Nemo-3.5-Lightning-v1

Table 1: Total and routed-expert parameters counted from the released tensors; active-per-token is non-routed

+

k/E

of routed. Gpt-OSS ships MXFP4-packed experts, so its figures are vendor-reported. NemotronH (Nano) and Nemotron-3.5-Lightning are Mamba/MoE hybrids (Mamba, MoE and attention layers of 52; the state-space line of Gu and Dao [14] , Dao and Gu [8] and the vendor’s Nemotron-H report [ 21 ] ); the other two are MoE transformers. The right-most column names the fine-tuned Sophea reasoning release each base produces; all four SFT releases are language-matched, and a fifth release, Sophea-Qwen3.6-v1.1, is the RLVR refinement of the Qwen row (§ 15.1 ). Throughout, base refers to the vendor checkpoint and fine-tuned to its Sophea reasoning model. Lightning is the next-generation release in the Nemotron line; Nano and Lightning share the 31.6B / 6-of-128 routing profile and are reported together as the Nemotron family (Nano is the same-generation member of that family).

domain (reasoning half)	rows	share
mathematics	22,608	38.2%
science	11,064	18.7%
world knowledge	5,884	10.0%
deductive logic	5,151	8.7%
medical	4,898	8.3%
commonsense	3,904	6.6%

Table 2: The reasoning half is math-heavy and thin on the axes we evaluate hardest: commonsense is

6.6\%

and logic

8.7\%

of it.

name	trained on	rows
Base	nothing (the released model)	—
Reasoning	reasoning rows only	59,107
Subset	a subset of the same rows	15,607
Two-Phase	reasoning rows, then $+$ the direct half	59,107 $+$
One-Phase	one pass over both halves at once	59,107 $+$

Table 3: Two-Phase continues the same adapter from Reasoning onto the hybrid mix; One-Phase never separates the two halves. The three recipes fail in different ways (§ 7 ).

Figure 1: fallback% (the rate at which a model never emits the requested answer line) by recipe and by language-matched checkpoint. It separates “cannot reason” from “will not answer in the requested form”: every recipe failure in the paper is visible here before it is visible anywhere else. The Qwen recipes are the side experiment of § 7 ; the Gpt-OSS language-matched arm’s pre-repair Greek-lane rate (70%) is the family defect at its largest (its traces are present and on-language; the model simply does not close with the line the scorer is told to read), and the released checkpoint’s format-repair dose cuts it to

26\%

(§ 11 ), still the highest of the releases. The dashed reference is a base model on the English control (

3.4\%

), showing the failure is not inherent to the benchmark.

Figure 2: Left: one configuration, three seeds, nothing else changed. Right: every accuracy effect we measured over the project, against that range. All of them fit inside it.

seed	mean	logic	fallback	trace Greek
42	76.2	56.2	10%	1.00
43	68.7	35.6	41%	1.00
44	76.4	54.0	3%	1.00

Table 4:

\text{sd}=4.4

pp, range

=7.7

pp. Note the last column.

Figure 3: Every accuracy effect we measured, against the

7.7

-point seed-to-seed band (shaded). Attribute-level effects (data selection and corpus versions) sit inside the band we would have been tempted to narrate. The only effect that survives is the replicated phase-2 comparison of § 7 (

+6.9

pp,

15

arms, permutation

p=0.0008

): it is inside the band geometrically, but it is estimated across

15

independently trained arms rather than read off a single run, which is precisely the difference the floor teaches. The language-matched accuracy cost (

-6.9

pp) is drawn as an open (unfilled) marker on purpose: it is a single-run measurement inside the floor, disclosed rather than interpreted, and not a recipe property. The equal magnitudes of the

+6.9

and the

-6.9

are a coincidence: different lanes, different arms.

phase-1 rows	selection	mean
62,562	all	69.5
15,607	top-by-structure	69.5
15,607	uniform random	69.0

Table 5: Selection buys

-0.5

pp (

0.31\sigma

); a

4\times

smaller pool costs nothing. The

62{,}562

is the reasoning count of the pre-gate pool the experiment was run against (

62{,}562

reasoning,

60{,}214

direct); the

59{,}107

quoted everywhere else is the post-gate train half (

59{,}107

reasoning,

58{,}985

direct), so the table’s all row and the paper’s reasoning-half count differ by the

3{,}455

rows the decontamination/dedup gate and the val reserve removed.

Figure 4: The same two checkpoints on four dimensions, the base and the structure-selected reasoning-only arm ( Subset , the “Reason-16k” of Figure 11 ), on the

1{,}000

-item probe lane. Only the first is accuracy.

	anchored accuracy			fallback %
arm	math	cs	logic	math	cs	logic
	$\uparrow$ higher is better			$\downarrow$ lower is better
Qwen base (Greek think)	92.9	82.4	42.6	3.3	0.5	0.2
Sophea-Qwen3.6-v1	83.3	75.8	37.3	18.2	22.9	46.3
Gpt-OSS base (Greek think)	90.7	63.2	32.2	2.4	5.2	0.7
before repair (reference)	74.6	53.7	25.2	34.9	83.5	77.4

Table 6: Per-domain results on the Greek think lane; here and in every table,

\uparrow

marks metrics where higher is better and

\downarrow

where lower is better (math = the two math slices, commonsense = HellaSwag+Winogrande, logic = ProofWriter macro-recall). Rows are grouped by family, each release indented under its own base, so every legitimate accuracy delta is a within-block comparison and the cross-base reading (the one an earlier draft of this paper fell for) is structurally discouraged. Every release now carries its own base row: the NemotronH base dump is the same vintage as the Qwen base dump, and the Nemotron-3.5 base lane, absent until this revision, was generated with the identical lane settings and scored with the same scorer as every other row. The same base is also measured on the NLU retention suite (Table 14 , Greek macro

57.5

), a direct-mode instrument whose numbers are not comparable to this think-lane table. The Nano release also appears in the retention suite (Table 13 ), and its fallback shape is the mirror of Gpt-OSS’s: negligible on math and commonsense (

1.7

/

1.8\%

) but

39.9\%

on logic, so its

9.8

logic macro-recall is substantially a format floor on the one domain where its answer form breaks. Accuracy and fallback move together: every release keeps the base’s domain ordering, and the Sophea-OSS format-repair dose pulls the commonsense fallback back toward math’s level (

83.5{\to}25.7

) while cutting logic’s by a third (

77.4{\to}50.9

). The pre-repair row is reference only; the release is the repaired arm. Conditioning on rows that emit the requested answer line (excluding the fallback-scored path) shrinks the apparent deficits sharply: the Qwen release reads

95.8/78.8/39.5

against its base’s

96.0/82.8/42.7

, and the same condition makes Gpt-OSS pre-repair

74.6\to 92.2

/

53.7\to 60.2

/

25.2\to 36.5

and Sophea-OSS-v1

78.6\to 94.4

/

53.3\to 58.2

/

26.5\to 30.0

(math/commonsense/logic), so a substantial share of every raw gap is format compliance, not reasoning.

arm	(%) $\uparrow$	(%) $\uparrow$	/100 sent. $\downarrow$	(%) $\downarrow$
	Greek fid.	EN ctrl	switches	leak
Qwen base	0.0	100	16.4	15.6
Qwen lang-matched	98.0	100	0.0	3.5
Qwen one-dir. †	98.7	99.3 §	0.0	1.5
Gpt-OSS base	0.0	100	9.7	0.5
Gpt-OSS lang-matched	98.1	100 ‡	0.0	10.3

Table 7: Language-matching across the three families: all four released checkpoints, the two measurable bases, and the one-directional reference arm. Greek fid. : Greek-trace fidelity on the Greek benchmark; both bases sit at exactly

0.0

(

0/5{,}156

traces reach ratio

0.9

), and all four language-matched checkpoints land at the same level (

97.4

–

98.1\%

). EN ctrl : English-control compliance,

100\%

for every arm. Switches : in-question language switches per

100

sentences on the Greek lane; the bases switch

16.4

(Qwen) and

9.7

(Gpt-OSS) times, every fine-tuned arm (the one-directional reference included) zero. The base fidelity and switch cells are one instrument run: both base dumps rescored with the current scorer on the same day (an earlier draft printed

19.0

for the Qwen base from an earlier dump vintage). Leak : answer-channel leak, the one place families differ (

10.3\%

Sophea-OSS-v1,

5.2\%

Lightning,

3.5\%

Qwen,

0.0\%

Nano, the only arm at zero; base leak is measured on the English control, fine-tuned leak on the Greek think lane). † Arms of the earlier one-directional recipe, shown for reference (a different recipe, not releases); the Qwen one-directional row is the recipe-evolution comparison: it locks against explicit language instructions (Table 10 ), not against English questions. § Measured on the

1{,}900

-item paired probe (

99.3\%

English,

0.0\%

Greek,

0.7\%

too short to score); this arm was never run on the

1{,}100

-item control the other EN-ctrl cells use. ‡ Measured on the pre-repair language-matched arm: the repair dose adds

2\%

trace-less answer-format rows and does not touch trace language, but the released arm’s own English control has not been generatively re-run. The recipe generalises to every family tried; the leak’s size does not.

Figure 5: Median trace length in tokens (each family’s own tokenizer,

5{,}156

matched items), base against the fine-tuned release. The word-level shortening must repay Greek’s

2.3

–

2.5\times

token fertility before it becomes a serving saving: Qwen’s does, NemotronH’s breaks even, Gpt-OSS’s does not.

Figure 6: Overrun

=

trace

\geq 3\times

the median trace for that item across arms. Easy

=

at least

80\%

of arms answer correctly. Cells pool the arms of each column; hard-item think-mode rates are compressed by generation-cap truncation (§ 5 ), so the easy row carries the section’s claims.

recipe	mean	empty	fallback
One-Phase	66.1	23.6%	12%
Two-Phase	64.4–69.9	0.0–1.3%	33–40%
reasoning-only	73.6	0.0%	2–12%

Table 8: single-phase = one pass over both halves; two-phase = reasoning-only then hybrid; reasoning-only = the reasoning half alone. Empty-trace rate is measured in think mode, fallback% in direct mode; means are over all arms of each recipe. Fallback ranges are over the individual arms of each recipe (2–12% across the three reasoning-only arms; 33–40% across the two-phase arms); the three seed replicates of § 4 widen it further (3–41% across seeds 42–44, all three of which are among the six phase-1-only arms above), so the fuller picture is 2–41% when seed noise is admitted.

Figure 7: Each point is an independently trained arm. Corpus version varies within both groups.

Figure 8: The four withdrawn findings of this section, drawn: the effect as first believed (open circle) against the same quantity after its control (filled). Three flip sign; the un-anchored scorer artifact (

+29.8

pp,

18\sigma

, one probe) collapses entirely: its endpoint is drawn at zero because the prose reports the artifact removed by anchored scoring, not as a measured residual. Rows are different instruments and lanes, so magnitudes compare within a row, not across rows; the other pairs’ numbers appear in this section’s prose. The section’s fifth failure (§ 8.5 ) is a scorer defect rather than an effect-size withdrawal, so it has no before/after pair to draw.

arm	human	machine	$\Delta$
Base	95.6	90.7	$-4.9$ ( $-3.14\sigma$ )
Subset	94.0	95.1	$+1.1$
Reasoning	93.6	95.3	$+1.7$
Two-Phase	87.6	90.4	$+2.8$

Table 9: The base is

\sim

5 pp worse on our translations; every fine-tune is slightly better.

Figure 9: Trace-language composition per lane, Qwen line: the share of traces that are Greek (character ratio

\geq 0.9

), mixed, or English (

\leq 0.1

), on the same instrument as Table 7 ’s fidelity column. Greek-question rows are the full

5{,}156

-item benchmark; English-question rows are the

1{,}100

-item control (base) and the

1{,}900

-item probe (matched). Two honesty notes drawn rather than hidden: the base’s Greek-question traces are not mostly pure English: only

4.2\%

are; the mass is mixed script with English scaffolding (median ratio

0.33

, § 5 ); and the one-directional arm’s Greek-question lane decomposes as

98.7

Greek /

1.3

mixed /

0.0

English on the same instrument (

n{=}5{,}156

, uninstructed); its English-question lane, generated later on the same

1{,}900

-item probe, measures

99.3\%

English (Table 7 ). Uninstructed defaults only; the instructed override is Table 10 .

model, instruction	English traces	Greek traces
base, “reason in English”	100.0%	0.0%
base, “reason in Greek”	72.0%	0.0%
fine-tune, “reason in English”	0.0%	98.7%
fine-tune, “reason in Greek”	0.0%	98.7%
matched ft, “reason in English”	44.8%	24.8%
matched ft, “reason in Greek”	0.0%	98.5%

Table 10: The one-directional fine-tune’s traces are identical under both instructions, and it does not matter how the instruction is phrased. Rows classified as neither/mixed scripts (

28\%

of the base under the Greek instruction) are omitted, so rows need not sum to

100

. The matched ft rows, measured after the language-matched re-training of § 10 , show the override partially returning; its mixed share under the English instruction is

30.4\%

.

released checkpoint	“in English” $\uparrow$	“in Greek” $\uparrow$
	(Greek Q)	(English Q)
Sophea-Qwen3.6-v1	44.8%	83.7%
Sophea-Qwen3.6-v1.1 (RLVR)	53.9%	85.7%
Sophea-OSS-v1	62.5%	93.3%
Sophea-Nemo-3.5-Lightning-v1	0.0%	92.7%
Sophea-Nemo-3-Nano-v1	0.0%	87.5%

Table 11: Instructed-override compliance on the five released checkpoints: the share of traces in the instructed language when the instruction contradicts the question (

n{=}1{,}000

Greek-question and

n{=}1{,}100

English-question items; same scorer as Table 10 ). Every release obeys “switch to Greek”; only the Qwen line and Gpt-OSS obey “switch to English”, and the RLVR refinement widens Qwen’s opening (

44.8\to 53.9\%

, § 15.1 ). Two instrument notes, both checked: the Nemotron dumps do not separate trace from answer, so the mandatory Greek answer line falls inside the scored text: re-scoring with that line stripped leaves both Nemotron arms at

0.0\%

(the Qwen control moves

44.8\to 49.5\%

, the expected direction); and the Nemotron arms return

5

–

9\%

of rows too short to score against Qwen’s

0\%

, consistent with the low reasoning ceiling reported in § 13 .

arm ( $n{=}1{,}100$ )	accuracy	reasons in EN
Qwen base / ft	94.0 / 96.2	100% / 100%
Gpt-OSS base / ft	95.5 / 96.1	100% / 100%
NemotronH base / ft	92.7 / 93.7	100% / 100%
Qwen language-matched	94.4	100%
Gpt-OSS language-matched	91.9	100%

Table 12: English questions get English traces from every arm measured. The fine-tunes in the first three rows are the dual-mode / reasoning-recipe checkpoints (§ 7 ), kept deliberately: they are the recipe-class evidence that one-directional training does not lock English questions . The last two rows are the language-matched checkpoints: the released Qwen checkpoint, re-run on this exact control (

94.4

,

+0.4

over its base,

100\%

English traces, consistent with its

1{,}900

-item probe above), and the Gpt-OSS language-matched arm evaluated after merging (the pre-repair arm; the released, format-repaired checkpoint’s control has not been generatively re-run, Table 7 ). Together the rows confirm the language-matching result is not Qwen-specific; none of this retests the hard one-directional lock of Table 10 , which remains a property of training in a single output direction.

Greek macro	base	language-matched	+ format repair
Gpt-OSS	$61.2$	$53.9$	$\mathbf{58.0}$
macro deltas vs base (points):
Sophea-Qwen3.6-v1	$-0.01$ Greek / $+0.08$ English
Sophea-OSS-v1	$-3.2$ Greek / $+1.0$ English †
Sophea-Nemo-3-Nano-v1	$+3.8$ Greek / $-2.0$ English
Sophea-Nemo-3.5-Lightning-v1	$+1.7$ Greek / $-1.1$ English

Table 13: Forgetting on the Titan-1 suite. Top: Gpt-OSS on the nine Greek non-reasoning benchmarks, base against the language-matched fine-tune, before and after the format-repair dose: the language-matched fine-tune initially costs

-7.3

points, and the repair dose recovers

4.1

of them, so the residual is

-3.2

not zero. Below: the macro deltas this paper reports per family, fine-tuned arm vs its own base; the format-repaired Gpt-OSS is the fine-tuned arm we stand behind . Qwen is flat in both languages; Nano gains Greek (+3.8) against the family’s low 0.49 Nano base (mostly catch-up, not forgetting); Lightning is read against its own base, measured separately (Greek macro 57.5, well above Nano’s 48.6), and gains +1.7; an earlier +10.6 reading against the shared Nano base is superseded by that measurement. Per benchmark against binomial standard error; a macro delta is a direction, not a test. † On English the pre-repair language-matched Gpt-OSS lost

-7.7

points (

75.7\to 68.0

); re-measured on the repaired release, the English macro is

76.7

,

+1.0

above base: the repair dose recovered both lanes, so the pre-repair loss was format behaviour throughout. Macro deltas are computed on unrounded macros; Table 14 prints one decimal, so its Qwen columns round to

75.7{\to}75.6

Greek /

85.1{\to}85.2

English.

Greek (nine benchmarks, the Greek macro of Table 13 ):
	Qwen		Gpt-OSS		Nemotron
benchmark	base	ft	base	ft	Nano base	Nano ft	Ltng. base	Ltng. ft
ARC-Challenge-el	91.8	92.0	77.1	68.6	54.2	59.0	68.7	72.8
ARC-Easy-el	96.9	97.1	85.4	79.9	62.3	70.1	75.8	81.4
Belebele-el	92.7	93.7	86.0	81.3	67.0	78.0	81.3	83.3
GreekMMLU	83.8	83.0	66.3	61.3	58.8	53.3	64.2	62.2

Table 14: Every benchmark behind the macros of Table 13 (Titan-1 suite, non-reasoning mode; accuracy %). Qwen ft is Sophea-Qwen3.6-v1 (language-matched); Gpt-OSS ft is Sophea-OSS-v1, the format-repaired release (its pre-repair intermediate is in Table 13 ); Nano ft is Sophea-Nemo-3-Nano-v1, the language-matched Nano release; Lightning ft is Sophea-Nemo-3.5-Lightning-v1 (language-matched); the two Nemotron generations share the same 31.6B / 6-of-128 routing profile (Table 1 ) and each is read against its own base column. (The earlier one-directional recipe arm is reported only in § 13 ’s recipe history, not as a release.) The per-benchmark view localises the Gpt-OSS story: before the repair the loss concentrated on the instruction-format-heavy reading benchmarks (Belebele-el

-17.7

, ARC-Challenge

-13.1

el /

-19.1

en, MMLU-en

-16.0

) while HellaSwag and Winogrande gained in both languages, and the repair dose recovers most where the loss was format (Belebele-el regains

+13.0

of the

17.7

). English retention on the released Gpt-OSS checkpoint recovers to

+1.0

above its base; Lightning retains English within

1.1

points of its own base.

Figure 10: Every per-benchmark retention delta behind Table 14 : fine-tuned release minus its own base (Lightning against the Lightning base; Sophea-OSS-v1 is the format-repaired release), Titan-1 suite, non-reasoning mode. Per-benchmark binomial sigma applies and a delta is a direction, not a test (Table 13 ); the value is the pattern: the Qwen column is near-white in both languages, the Sophea-OSS-v1 residual concentrates on the knowledge-heavy benchmarks (MMLU-el

-10.1

) while HellaSwag gains in both languages on every family, and MMLU is the largest English loss for all three non-Qwen releases.

Figure 11: Left: accuracy per axis on the 5,156-item benchmark for the Qwen recipe arms (the cross-family releases are Table 6 ), with the seed-to-seed noise floor shaded around the Qwen base. Right: the same arms’ cost in words per correct answer (token-denominated cost is Figure 5 ). Math uses the 250 human-translated items only (§ 8 ); logic is macro-recall (§ 3 ).

Supported by our evidence	basis
Drop the hybrid phase 2; train reasoning-only	+6.9 pp over 15 runs, $p=0.0008$ (§ 7 ); and the direct mode it sacrifices costs nothing measurable on direct-mode NLU (E3, § 11 )
Generate exploratory traces, not explanations	structure $0.27\to 0.68$ on the same questions
Forbid formulaic openings in the generator	24/24 identical openings without it
Report fallback% beside accuracy	separates reasoning from format compliance
Run the seed control before any ablation	7.7 pp (§ 4 )
Probe trace-language steerability per checkpoint	same recipe, four checkpoints, $62.5$ / $44.8$ / $0$ / $0$ (§ 10.1 )

Table 15: Supported and unsupported configuration choices, each with its evidential basis.

	Greek		fall-		override	reverse
arm	fid. % $\uparrow$	acc $\uparrow$	back % $\downarrow$	leak % $\downarrow$	el $\to$ EN % $\uparrow$	en $\to$ EL % $\uparrow$
SFT checkpoint (before)	97.98	73.7	24.1	3.53	44.8	83.7
random reward (control)	98.06	74.0	22.1	3.61	44.1	83.8
correct+format+term.	98.22	77.2	2.5	0.00	48.8	84.7
+ language	98.02	77.2	5.6	0.04	45.9	84.5
+ override	98.27	77.0	2.8	0.02	53.9	85.7

Table 16: The pre-registered RLVR round, scored on the held-out instruments (Greek think lane

n{=}5{,}156

; override lanes

n{=}1{,}000

/

1{,}100

), same scorer, same day. The before row is the released checkpoint’s frozen dump rescored with the current scorer; its full same-day regeneration reproduced all

5{,}156

responses bit-identically (greedy decoding is deterministic here), and the plain-mode lanes to the decimal (

44.8

/

83.7

): the before column is not a provenance caveat but a verified constant. The control row is the entire admissibility argument: on every axis the random-reward arm reproduces the baseline, so nothing below can be elicitation. Accuracy is shown for completeness only:

+3.3

–

3.5

is inside the

\pm 7.7

seed floor of § 4 and is claimed by nobody.

Thinking in a Low-Resource Language: What SFT Builds, What RL Fixes, What Accuracy Cannot See

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