Priming Base Models for Agentic Post-Training: An Open Midtraining Recipe

Soumye Singhal; Joseph Jennings; Ka Shun Shum; Alexander Bukharin; Khushi Bhardwaj; Zirui Liu; Venkat Srinivasan; Jonathan Li

cs.CLOct 15, 2026

Priming Base Models for Agentic Post-Training: An Open Midtraining Recipe

Authors: Soumye Singhal, Joseph Jennings, Ka Shun Shum, Alexander Bukharin, Khushi Bhardwaj, Zirui Liu, Venkat Srinivasan, Jonathan Li,

Organizations: NVIDIA

Abstract

Frontier models, general-purpose and domain-specific alike, now include a midtraining stage between pretraining and post-training, but little has been published about how this stage is built. We present an open midtraining recipe that primes base models for agentic post-training, and we show its effectiveness on two Nemotron models of different sizes. Pretraining typically runs at short context and sees almost no agent interactions, while SFT data is mostly synthetic, and very long SFT stages can hurt the model's later RL trainability. Our midtraining stage sits between the two. It trains at 256K context for hundreds of billions of tokens on a diverse mixture of natural software-engineering data like packed repositories, pull requests, commit histories, and build logs together with a small share of agentic traces. On Nemotron 3.5 Lightning, 500B midtraining tokens improve SWE-bench Verified from 60.7 to 64.5, SWE-bench Multilingual from 55.8 to 62.8, and SWE-bench Pro from 32.0 to 35.5 after identical full SFT. On the larger Nemotron 3.5 Super, 280B midtraining tokens improve DeepSWE, a harder benchmark of long-horizon tasks, from 11.7 to 15.7 and SWE-bench Verified from 72.9 to 74.3 after identical full SFT. Midtraining also improves robustness across agent harnesses on both models. We report the ablations behind the recipe and an evaluation protocol built on short fixed SFT runs, since base-model proxy metrics did not correlate reliably with post-training results.

Figures & tables

Panel of Figure 1 — Figure 1: The midtraining blend, by share of training tokens. Replay is drawn from the base model’s pretraining data. Token counts and epochs per source are in Table 1 .

Source	Unique tokens	Share of blend	Tokens seen (500B run)	Epochs
Issue–PR	216.9B	40.8%	205.2B	0.95
Repositories	104.1B	20.0%	100.7B	0.97
Agentic traces	39.4B	10.0%	50.3B	1.28
Diff data	16.0B	3.0%	15.1B	0.95
Build and CI logs	6.5B	1.2%	6.1B	0.95
Pretraining replay	$\sim$ 2.0T	20.0%	100.7B	0.05

Table 1: Sources of the midtraining blend in Figure 1 . Replay is drawn from the base model’s final-phase pretraining blend. Epochs are tokens seen divided by unique tokens.

Short SFT (33B tokens)
	SWE-V	SWE-Multi	TB 2.0
	pass@1 / pass@3	pass@1 / pass@3	pass@1 / pass@8
Base-8K	57.1 / 69.2	48.6 / 65.0	22.1 / 40.5
Midtrained, no traces	56.1 / 68.2	48.4 / 65.3	19.8 / 40.5
Midtrained	57.9 / 71.4	56.8 / 70.0	20.1 / 39.3
Full SFT (100B tokens)

Table 2: Early experiment on the N-3.5 Lightning-8K checkpoint, midtrained for 100B tokens with and without agentic traces and compared with the base model after identical short or full SFT (%).

Midtraining	SWE-V	SWE-Multi	TB 2.0	SWE-Pro
tokens (B)	pass@1 / pass@3	pass@1 / pass@3	pass@1 / pass@8	pass@1 / pass@3
0 (base)	60.7 / 72.2	55.8 / 72.3	22.6 / 43.8	32.0 / 41.5
100	64.1 / 74.0	57.4 / 71.7	22.5 / 44.9	34.9 / 45.1
200	64.3 / 73.4	59.7 / 76.7	21.4 / 39.3	35.8 / 45.3
300	63.5 / 73.6	62.0 / 76.7	24.9 / 48.3	36.3 / 46.4
500	64.5 / 75.0	62.8 / 77.0	23.6 / 46.1	35.5 / 45.4

Table 3: Midtraining scaling on N-3.5 Lightning-LC after identical full SFT. After 500B tokens, midtraining improves both pass@1 and pass@

k

on every benchmark, so it raises what the model can solve rather than only its reliability.

	LoCoDiff		DeepSWE	Repository	RepoBench
	Exact (%) $\uparrow$	Distance $\downarrow$	BPB $\downarrow$	BPB $\downarrow$	$\uparrow$
N-3.5 Lightning-LC	0.0	0.903	0.171	0.238	70.09
Midtrained, 100B	21.5	0.228	0.144	0.228	71.22
Midtrained, 500B	26.8	0.164	0.140	0.217	72.41

Table 4: Base-model post-trainability metrics for N-3.5 Lightning-LC and its midtrained checkpoints from the scaling run. All metrics improve as midtraining scales.

Short SFT (33B tokens)
Midtraining	SWE-V	SWE-Multi	SWE-Pro	DeepSWE	BrowseComp
0 (base)	71.3	65.9	52.1	0 6.4	64.2
65B	71.9	65.6	51.7	10.4	65.0
280B	72.1	67.4	52.2	12.4	67.2
Full SFT (100B tokens)
0 (base)	72.9	69.0	53.9	11.7	61.8

Table 6: N-3.5 Super after 65B and 280B midtraining tokens and identical short or full SFT (pass@1, %). Midtraining gives a large gain on DeepSWE, the hardest benchmark, and also improves SWE-bench Verified and BrowseComp.

	N-3.5 Super, short SFT						N-3.5 Lightning-8K, full SFT
	SWE-bench Verified			Terminal-Bench 2.1			SWE-bench Verified
Harness	Base	Mid 65B	$\Delta$	Base	Mid 65B	$\Delta$	Base	Mid 100B [-1pt](no traces)	Mid 100B	$\Delta$
Pi	57.4	67.8	+10.4	30.3	35.2	+4.9	45.7	48.8	59.9	+14.2
OpenCode	54.4	65.8	+11.4	35.2	38.2	+3.0	52.0	50.1	63.1	+11.1
Hermes	59.8	63.2	+3.4	44.2	53.9	+9.7	43.3	43.3	50.0	+6.7
Codex	48.4	61.8	+13.4	27.0	39.7	+12.7	42.1	45.9	61.1	+19.0

Table 7: Midtraining improves pass@1 on every agent harness we evaluated, for both models, with the largest gain on the harness where the base model is weakest (%).

	8K	16K	32K	64K	128K	256K
8K $\rightarrow$ long-context @1M	94.1	91.9	90.5	85.9	81.3	76.6
8K $\rightarrow$ midtraining @256K	92.3	91.4	87.8	81.9	76.1	62.2
8K $\rightarrow$ midtraining @256K $\rightarrow$ long-context @1M	92.1	91.0	89.8	86.6	83.8	79.7

Table 8: N-3.5 Lightning RULER scores up to 256K. Midtraining alone brings N-3.5 Lightning-8K close to N-3.5 Lightning-LC up to 128K, acting like a long-context stage.

SWE-V	pass@1	pass@3
Unmasked	27.5	41.6
Masked	37.8	53.0

Table 9: Tool-response masking. N-3.5 Lightning-8K midtrained for 100B tokens with and without tool responses in the loss, followed by identical SFT with an earlier version of our SFT recipe (SWE-bench Verified).

Table 10

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 2: Document lengths in tokens for each source in the midtraining blend (log scale).

Language	Records	Tokens (B)	Natural share (%)	Blend weight (%)
Python	1,798,331	24.51	23.5	31.0
Java	945,513	18.71	18.0	17.0
TypeScript	1,273,349	13.20	12.7	12.0
C#	460,597	11.66	11.2	10.0
JavaScript	1,289,843	10.01	9.6	8.6
C++	400,540	8.27	7.9	7.6

Appendix

Table 12: Repository data by language. Tokens are measured with the model tokenizer.

Type	Examples	Document tokens	Trained tokens	Trained share
Multi-file edits	733,705	13.3B	6.8B	51%
Code evolution	377,618	3.5B	3.2B	91%
Total	1,111,323	16.8B	10.0B	60%

Appendix

Table 13: Diff data used in our experiments. Trained tokens exclude masked context. Counts use the tokenizer of our data pipeline. With the model tokenizer, the total is 16.0B (Table 1 ).

Scaffold label	Trajectories	Share (%)
codex	166,624	19.39
openhands	120,086	13.98
generic_code_cli	107,291	12.49
gemini_cli	104,717	12.19
terminus	99,915	11.63
taubench_react	50,399	5.87

Appendix

Table 14: Trajectories by scaffold label in the early snapshot. Shares are of trajectories, not tokens.

Tool name	Calls	Share (%)
bash	3,659,199	19.6
execute_bash	3,633,328	19.4
str_replace_editor	2,913,745	15.6
keystrokes	2,029,095	10.9
exec_command	736,655	3.9
write	524,113	2.8

Appendix

Table 15: The ten most frequent tool names in the early snapshot, as each scaffold records them, so similar tools such as bash and execute_bash are listed separately. Shares are of all tool calls.

Assistant turns	Trajectories	Share (%)
1	146,334	17.03
2	56,929	6.63
3	47,362	5.51
4–5	77,700	9.04
6–10	138,036	16.07
11–20	111,777	13.01

Appendix

Table 16: Trajectory length in assistant turns, in the early snapshot.

	N-3.5 Lightning midtraining	N-3.5 Super midtraining	SFT (both models)
Parameters (total / active)	30B / 3B	120B / 12B	–
Sequence length (packed)	256K	256K	256K
Global batch size (sequences)	64	64	64
Peak learning rate	$5\times 10^{-5}$	$1\times 10^{-5}$	$2\times 10^{-5}$
Warmup (sequences)	32K	30K	–
Schedule after warmup	constant	constant	cosine to $5\times 10^{-6}$

Appendix

Table 17: Training settings. TP, CP, and EP are tensor, context, and expert parallelism, and MTP is multi-token prediction.

SWE-V (pass@1 / pass@3)	SFT	RL step 10	Step 15	Step 20
Base-8K	61.2 / 71.8	60.4 / 70.8	61.4 / 72.2	60.4 / 71.7
Midtrained-100B	63.2 / 74.1	63.0 / 74.0	63.5 / 73.7	63.0 / 73.0
TB 2.1 (pass@1 / pass@8)	SFT	RL step 10	Step 15	Step 20
Base-8K	23.0 / 46.1	24.7 / 44.9	22.9 / 41.6	20.8 / 39.3
Midtrained-100B	24.2 / 44.9	23.5 / 44.9	24.4 / 44.9	23.2 / 46.1

Appendix

Table 18: RLVR from N-3.5 Lightning-8K and its midtrained checkpoint after full SFT. SFT-start scores are re-evaluated with the RL checkpoints. The midtrained model leads on SWE-bench, and its Terminal-Bench pass@8 holds.

Benchmark	Rollouts	N-3.5 Lightning-LC	Midtrained, 500B
SWE-bench Verified	1,500	40	26
SWE-bench Multilingual	900	25	14
SWE-bench Pro	2,193	91	13
Terminal-Bench	712	8	1

Appendix

Table 19: Rollouts that fail by filling the 256K context window, after identical full SFT.

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