Common instruction following (IF) benchmarks, such as IFEval, evaluate the ability of language models (LMs) to follow simple constraints that can easily be verified programmatically. These same constraints, in turn, are used as reward signals for reinforcement learning. This design may lead to reward hacking of constraints, pushing models away from the main task. Recent attempts to address this resulted in benchmarks with richer constraints, but required expert labor, remained closed-access, and cannot be easily extended. We present Lift-Bench, an IF benchmark that asks a model to draft a document given a topic and a structured outline. Constraints are scoped to certain document sections, providing finer control over constraint density. Lift-Bench includes both verifiable and judged constraints, as well as a training counterpart, Lift-Train. Importantly, Lift-Bench provides both main task and instruction-following constraints, mitigating shallow reward hacking. On experiments with multiple models, we find that frontier models struggle with the harder subsets of our benchmark and analyze model performance along multiple axes. We also find that training on Lift-Train is much less susceptible to reward hacking than training on the common IFTrain. Lift-Bench and Lift-Train are publicly available, as well as the data generating pipeline.
Figures & tables
Data
Constraints
Released
Benchmark
Size
Authoring
Style
Content
Max
Scope
Train set
Pipeline
IFEval
541
manual
✓
✗
3
global
✗
✗
IFBench
300
manual
✓
✗
2
global
✓
✗
ComplexBench
1,150
manual
✓
✓
5
global
✗
✗
AdvancedIF
1,645
manual
✓
✓
10
global
(✓)
(✓)
MOSAIC
4,000
synthetic
✓
✗
20
global
✗
✓
Table 1: Comparison with related IF benchmarks. (✓): exists but not publicly released.
Figure 2: Synthesis pipeline overview.
GPT
Claude
Open Models
split
5.5
5.6 Sol
5 nano
Fable 5
Opus 4.8
DS V4 Pro
GLM-5.3
Kimi K3
Qwen3.8-A95B
short/few
0.97
0.95
0.71
0.92
0.94
0.95
0.87
0.97
0.89
short/some
0.96
0.94
0.62
0.91
0.92
0.95
0.88
0.96
0.88
short/many
0.98
0.94
0.58
0.82
0.89
0.93
0.83
0.97
0.82
short/numerous
0.93
0.92
0.47
0.81
0.82
0.92
0.80
0.92
0.81
long/few
0.98
0.96
0.67
0.92
0.96
0.95
0.93
0.96
0.90
Table 2: Mean score per split; best per row in bold; averages are ± 95% CI; ‘avg (all)’ weighs the structured and flat averages equally.
Figure 3: Benchmark scores broken by number of judged and verifiable constraints. In the lightest cell (bottom left) GPT-5.5 and GPT-5 nano both score near 1, but at the densest loads the weaker model falls to 0.2–0.5 while the stronger stays above 0.7.
% score change by document size
Model
short
long
mean
Kimi K3
−0.9 %
+0.2 %
−0.4 %
Claude Opus 4.8
−0.4 %
−0.5 %
−0.4 %
GPT-5.6 Sol
−0.4 %
−0.9 %
−0.7 %
GPT-5.5
−1.3 %
−1.7 %
−1.5 %
GPT-5 nano
−7.0 %
−4.1 %
−5.5 %
Table 3: Change in section score, estimated via linear regression and averaged over density types. At fixed per-section density, frontier models are only mildly affected by constraints elsewhere in the document while smaller models continue to degrade. The full results are in Table 11 and the fitting procedure in Appendix I .
Figure 4: Benchmark scores per constraint type. Models display distinct constraint-type fingerprints, even where their averages are close.
Benchmark
base
IFTrain
LIFT-Train
IFEval
0.712
0.764 ( +5.2 )
0.723 ( +1.1 )
IFBench
0.270
0.430 ( +16.0 )
0.278 ( +0.8 )
LIFT-Bench (10-split avg)
0.423
0.451 ( +2.8 )
0.822 ( +39.9 )
AdvancedIF (3-split avg)
0.392
0.374 ( −1.8 )
0.401 ( +0.9 )
ComplexBench (EN)
0.324
0.316 ( −0.8 )
0.327 ( +0.3 )
Table 4: Benchmark scores after GRPO training on Qwen2.5-7B (step 180), with the change in points against the base model in parentheses. Shaded cells are in-distribution; other cells are out-of-distribution. Each recipe gains most on the benchmark its reward can see, but only LIFT-Train also improves the external judged benchmarks.
Figure 5: Comparison of training mixtures on Qwen3-32B: LIFT-Bench score at GRPO step 180, averaged over the flat splits, the structured splits, and their macro-average (mean of the flat and structured averages).
Appendix figures & tables11 assets
Supplementary material from the paper’s appendix.
Appendix
Family
Type
Candidate source
Judged
content
LLM-based
tables / list / code_snippets
LLM-based; often empty if not relevant to the topic
Verifiable
language
sampled over ten fixed languages
case_type
sampled over {upper, lower, title}
emoji_count
sampled over {in-range, more-than, less-than}
lexical_count
keyword count / must-appear / must-not-appear
Appendix
Table 5: Constraint types and the source of each block’s candidates. Keywords for lexical_count constraints are LLM-generated to make them relevant to the discussed topic and not arbitrary. We used ifbench_constraint for ablations, but not in the final splits. Shared hyper-parameters are in Table 7 .
Document
Scope
Constraints / section
Examples
Split
Verbalization
branching
depth
sections
verifiable
judged
test
train
short / few
structured
3
2
3
1–2
1–2
85
3,681
short / some
structured
3
2
5
1–3
1–2
85
3,681
short / many
structured
3
2
7
1–3
1–3
85
3,681
short / numerous
structured
3
2
7
1–4
1–4
85
3,681
long / few
structured
5
2
5
1–2
1–2
66
2,978
Appendix
Table 6: The ten released splits and the pipeline parameters that distinguish them. sections is M , the number of leaf sections one sampling pass may constrain; the two constraints / section columns give N per family, so a section receives between 1 and N constraints of that family. Every other pipeline parameter is identical across the ten splits, and the corresponding configuration keys are listed in Table 7 . Example counts are after failed items are dropped (see Yield ).
Stage
Parameter
Value
Seeding
seed corpus
Wikipedia Vital Articles, level 5
max_examples
4000
random_seed
1337
All LLM stages
model
Qwen2.5-32B-Instruct
temperature
0.6
max_tokens
2048
Appendix
Table 7: Pipeline hyper-parameters shared by all ten released splits; the four parameters that vary across splits are listed at the bottom, with their values in Table 6 .
Type
Mechanism
Example
doc_structure
Verifiable
Section headers must match the outline.
language
Verifiable
Write the text in Spanish.
lexical_count
Verifiable
Use the word “cards” 3–6 times.
emoji_count
Verifiable
Include at least 1 emoji.
case_type
Verifiable
Write the text in uppercase.
content
LM judge
Discuss the historical significance of Dogs Playing Poker in American pop culture.
Appendix
Table 8: Constraint types used in the drafting pipeline and their scoring mechanisms, with examples.
Table 9: Judge meta-evaluation against human labels ( n=305 ; 227 valid, 78 invalid). The original baseline judge is marked † . Every scoped judge beats every unscoped one, regardless of model.
Figure 6: Descriptive statistics for the released splits.
Table 10: Mean score per split for all 17 evaluated models, shown as two panels: the ten closed-weight models in ( 10(a) ) and the seven open-weight models in ( 10(b) ). Bold marks the best value across all 17 models, not the best within a panel. Averages carry a ± 95% CI across examples; ‘avg (all)’ weighs the structured and flat averages equally.
Figure 7: Fewer constraints, worse scores: the globally-scoped (flat) regime is harder than the scoped one and ranks models differently. Mean score ( ± 95% CI) at a matched constraint budget ( ≤ 10 total constraints), structured vs. flat
Table 11: Section-load slopes. Columns as in Table 3 .
Figure 9: Rollout health during GRPO training on Qwen2.5-7B: the lexical-only reward is the one that gets hacked. Held-out validation pass every 10 steps ( 200 completions per point).