Measuring Audio's Impact on Correctness: Audio-Contribution-Aware Post-Training of Large Audio Language Models
Authors: Haolin He
Xingjian Du
Renhe Sun
Zheqi Dai
Yujia Xiao
Mingru Yang
Jiayi Zhou
Xiquan Li
Zhengxi Liu
Zining Liang
Chunyat Wu
Qianhua He
Tan Lee
Xie Chen
Wei-Long Zheng
Weiqiang Wang
Mark D Plumbley
Jian liu
Qiuqiang Kong
Venue: ICLR 2026 Poster (2026)
Keywords: Large Audio Language Models
Audio-Contribution
Post-Training
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Large Audio Language Models (LALMs) represent an important frontier in multimodal AI, addressing diverse audio tasks. Recently, post-training of LALMs has received increasing attention due to significant performance improvements over foundation models. While single-stage post-training such as reinforcement learning (RL) has demonstrated promising results, multi-stage approaches such as supervised fine-tuning (SFT) followed by RL remain suboptimal. The allocation of data across multiple training stages to maximize LALM capabilities has not been fully explored, and large-scale, high-quality datasets for such research are also lacking. To address these problems, we firstly present AudioMCQ, a comprehensive audio multiple-choice question dataset comprising 571k samples with two kinds of chain-of-thought annotations. Secondly, we investigate the prevalent zero audio-contribution phenomenon in LALMs, where models derive correct answers solely from textual information without processing audio content. We propose Audio-Contribution Filtering to partition data into weak and strong audio-contribution subsets. Based on these insights, we develop two effective post-training paradigms: Weak-to-Strong (SFT on weak audio-contribution data followed by RL on strong audio-contribution data) and Mixed-to-Strong (SFT on mixed audio-contribution data followed by RL on strong audio-contribution data). We achieve first place in the DCASE 2025 Audio-Question-Answering challenge by using AudioMCQ. Additionally, leveraging our dataset with different training strategies, we achieve 78.2\% on MMAU-test-mini, 75.6\% on MMAU, 67.0\% on MMAR, and 71.7\% on MMSU, establishing new state-of-the-art performance.

Published as a conference paper at ICLR 2026
MEASURING AUDIO'S S IMPACT ON CORRECTNESS: AUDIO-CONTRIBUTION-AAAAR POST- TOST-IGGGNING OF LARGE AUDIO LANGUAGE MODELS
Haolin H Xingjian D Renhe Sun Zheqi Dai', Yujia Xiao*, Mingru Yang", Jiayi Zhou², Xiquan Li*, Zhengxi Liu', Zining Liang', Chunyat Wu', Qianhua He Tan Lee', Xie Chen', Wei-Long Zheng', Weiqiang WangS, Mark Plumbley", Jian Liu" 3.,, Qiuqiang Kongl.
The Chinese University Hong Kong. Hong Kong, China Uniiersity Rochester, USA
3 Ant Group, China *Shanghai Jiao Tong University, China
South China University of Technology, China "King's s College London, UK rex. 1j wejeanngrouu..... com. qakong@ee. cuhk. edu. hk
ABSTRACT
Large Audio Language Models (LALMs) represent an important frontier in mul- timodal AI, addressing diverse audio tasks. Recently, post-training of LALMs has received increasing attention due to significant performance improvements over foundation models. While single-stage post-training such as reinforcement learning (RI) has demonstrated promising results, multi-stage approaches such as supervised fine-tuning (SFT) followed by RI remain suboptimal. The allocation data across multiple training stages to maximize LALM capabilities has not been fully explored, and large-scale, high-quality datasets for such research are also lacking. To address these problems, we firstly present AudioMCQ, a com- prehensive audio multiple-choice question dataset comprising 571k samples with two kinds of chain-of -fhhoughht annotations. Secondly, we investigate the preva- lent zero audio-contribution phenomenon in LALMS, where models derive cor- rect answers solely from textual information without processing audio content. We propose ContriiConiiiiionnn Filtering partition data into weak and strong audio-contribution subsets. Based on these insights, we develop two effective post-training paradigms: Weak-to to-k-to--trong (SFT on weak cutioccontiiuution data followed by RL. on strong audio-contributio mixed audio-contribution data followed by RL on strong audio-contribution data). challenge by using AudioMCQ. Additionally, leveraging our dataset with different training strategies, achieve 78.2% on MMAU-test-ne 75.6% on MMAU, 67.0% on MMAR, and 71.7% on MMSU, establishing new state-of-the-an performance.
data) and Mixed-to-Stropp (SFT on
We achieve first place in the DCASE 2025
INTRODUCTION
In recent years, Large Audio Language Models (LALMs) have received increasing attention in the
field of multimodal artificial intelligence (Yang et al. 2025c Zhao al., 2023). These LALMSs are designed to handle diverse audio-related tasks, including Automatic Speech Recognition (ASR)
(Benzeghiba et al. 2007), Audio Captioning (AC) (Xu et al., 2023), and Music Captioning (MC) (Manco et al. 2021). ). among others. To achieve effective audio-text alignment in the latent space while developing multi-task execution capabilities, the pre-training of LALMs requires substantial computational resources and extensive datasets. Given these resource- ieoourc-intensive requirements, post- training of LALMS has emerged as an promising research direction.
Current post-training research for LALMs focuses two primary areas. First, recent work aims to enable LALMS to perform chain-of of-thouggtt (CoT) reasoning (Wang et al. 2025b). inspired by the success of OpenAl-ol (Jaech al. 2024). Several recent works have demonstrated this capability,
"First Authors; Corresponding Authors.

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Published as a conference paper at ICLR 2026
including Mellow (Deshmukh et al. 2025). Audio-CoT et 2025a), and Audio Reasone Xie iet al.] 2025), which integrate step-by-step reasoning into audio understanding tasks. The second fo- area involves leveraging reinforcement learning (RL) techniques enhance the performance of
LALMs. For instance, Qwen2-Audio (Chu et al. 2024) incorporates Direct Preference Optimiza- tion (DPO) (Rafailov et al. 2023) align models with human preferences. Group Relative Policy Optimization (GRPO), introduced by DeepSeek (Shao et al.) 2024), has also gained attention in recent research. RI-AQA (Li al. 2025) applies GRPO Qwen2-Audio using the 40k Audio- Visual Question Answering (AVQA) dataset (Yang et al. 2022). while Omni-R1 (Rouditchenko) al. 2025) scales RL to 1,700 steps by constructing 170k multiple-choice questions based on VG- GSound (Chen et al.) 2020) and fine-tuning Owen2.5-Omml (Xu et al.] 2025) using GRPO. Some recent works have combined both research directions. SART (Wen et al.) (025)) constructs 32k multiple-choice questions with chain-of-thoughe annotations and employs Curriculum-Guider RI. after supervised fine-tuning (SFT). Step- Aud-o2 (Wu et al. 2025a) implements multi-stage RL fol- lowing cold-start initialization: first applying Proximal Policy Optimization (PPO) (Schulman al. 2017) to enhance reasoning efficiency for real-time audio engagement, then utilizing GRPO to im- prove the model's audio perceptual capabilities. Audio-Thinke (Wu et al.. 2025b) enables hybrid reasoning capabilities through custom-designed rewards and large language model supervision.
However, several challenges persist in current studies. Despite utilizing more data, two-stage paradigms (e.g., SFT followed by RI) may not consistently outperform single-stage post-training, which imposes an effective upper bound on the data volume for post-training, Research on two- stage post-training for LALMs remains limited, which may be partly attributed to the scarcity of large-scale, high-quality datasets specifically designed for LALM post-training. To address these challenges, two primary contributions are made in our work. First, we present AudioMCQ, a high- quality audio multiple-choice question (MCQ) dataset comprising 571k samples, with each sam- ple containing both structured and unstructured CoT annotations, to facilitate improved research LALM post-training. Second, during the dataset construction, we investigate the tendency for LALMs to derive correct answers solely from textual information without processing the corre- sponding audio content. We categorize cases of audio-contribution into two types: Explicit Log- ical Reasoning and Implicit Knowledge Retrieval. Based on this observation, we perform Audio- Contribution Filtering (ACF) by categorizing our data based on the criterion "whether the question be answered correctly without listening to the audio," resulting in weak audio-contribution and strong audio-contribution splits. Through this filtering approach, we discover simple yet effective post-training paradigms: Weak-to-Strong, which involves SFT on weak audio-contribution data followed by GRPO on strong cudio-contribution data, and Mixed-to-Strong, which involves SFT on mixed
data followed by GRPO on strong audio-contribution data. Using Au- dioMCQ, we achieve first place globally in the DCASE 2025 Siciio--eestion--Ae AesetionnAAnseeeiiie Answering challenge Yang et al. al.,,2222.50 20256). Additionally, leveraging our dataset with different training strategies, we fine- tune Qwen2.5-Omni and establish new state-of-the-an art (SOTA) performance actoss multiple bench- marks. Specifically, the Weak-to-Strong strategy achieves 78.2% on MMAU-test-me test-mini and 75.6% on MMAU (Sakshi et al. 2025 while the Liked-to-Strong strategy attains 67. 7.0% on MMAR (Ma) et al. 2025b) and 71.7% on MMSU (Wang et al. 2025a). Both approaches yield significant perfor- mance improvements, and find that the effectiveness of each method on different benchmarks correlates with the inherent audio-contribution characteristics of downstream tasks.
This paper is organized follows: Section 2 presents the AudioMCQ dataset construction pipeline. Section 3 investigates the zero audio-contribution phenomenon in LAL.Ms and introduces Audio- Contribution Filtering to partition data into weak and strong audio-contribution subsets. Section demonstrates experimental validation of our dataset quality and proposes two effective post-training paradigms: Weak-to to Strong and Mixed-to- -oxee-to-Strong training approaches. Finally, we conclude with a discussion of our findings and their implications for future LALM research in Section 5
2 AUDIOMCQ DATASET
The overview of dataset construction is illustrated in Figure T Our data source selection follows two principles: (1) avoiding LALMs in source dataset construction to prevent hallucinations introduced by their reliance on textual queries, and (2) ensuring accuracy through human-verified or model-
All MMAU-test-mini and MMAU evaluations in this paper use version 05.15.25.
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Clobb, AadieCaps, MusicCaps LP LP-MasicCaps, Spech TACOS
2. Generate CoI based on templete
CompA-R
estogelo
According question text. the queshon for spacific
occurning during reoding requiring idenification ouditory claments related to the activity without reforencing
1. Genorate Question & Choices
audio quesition type sound.
He sightd os turned the pages the book
stopping the information.
estogels
"Question Formation
<Tiggez>
onelyze cudlo
Tat
Q1
Plesse descmbbe this music detail
sighed he
pages stopping information,
He sighed turred the pages the book
stopping to scon information
cudio content.
Fai
Quection Type Generation
Cholcrs
the
matches "Pages turning
person turning pages accompanied by which directll choices background" "A doge creaking and "Someone typing keys" the implied cadio description.
glow
person reading?
What specific sound oction occurs while the
"Pages turning with
<stloge3>
Question type: sound
"Music playing in bockground"
Key Logie Extraction Length Simplifiiation
"A door cresking open"
*Someene typing on keys"
*Pages turring with sight
caimple andlysis- sound type question caking
sound
happens person reeding
desembes person sighing
pages incicating
main
directly
Answer Corsistency 5
Gradlng
sigh creaking open," and choioss like playing bookyr ound!" scone. Since question focuses
the oction. correct answer "Pgees fumiing sigh" becnuse
pageeturring
g.
Incerrett Options Quality:
lyping
Language Fluency 4
port of directly reading, only motching sound provided "Pages turring sight" shot
Reasoning Logic Fidelity:
Simple Reasoring Logic Fidelity: 5
c/simple aralysiss
3. Generate Simple Co7
FILTER
KEEP
Only for No-question Data
Qwent-23811
Grade Fllicr
Overview of dataset construction. Detailed prompts are provided in Appendix mation on the in-pipeline quality check is provided in Appendix
Figure 1:
B
Infor-
refined pipeline. The training splits from Clotho (Drossos et al.
2020), AudioCaps (v2.0) (Kim et al.,
2019.
CompA-R (Ghosh et al. (2024). MusicCaps (Agostinelli et al. 2023), LP-MusicCaps (MTT
split)
Doh et al. (2023). SpeechCraft (LibriTTS-R split) (Jin et al. 2024), and TACOS (Primus et al.)
2025
are used, as summarized in Table [
2.1
BASIC QUESTION-ANSWER PAIRS FORMATION
Six of the seven source datasets contain only audio-caption pairs and lack native question-answer (Q-A) pairs. Therefore, they are transformed into unified Q-A format for consistent training. For- mally, given a source dataset Dar {aa, G) j where a; denotes audio samples, e; represents corresponding captions or annotations, and K is the number of samples, the transformation pro- cess converts all datasets into a unified format of Dunitied = {(a qe K where q: denotes the generated question for sample i.
The transformation process applies different functions for different datasets. For captioning datasets, the transformation function fcaption (a;, G3) -> (ai, Qemplaate C;) is applied, where Qeemplate represents one of five predefined question templates described as follows: "Please describe this audio in detail"
Table I: Overview of our source datasets. AC: Audio Captioning, AQA: Audio Question Answering, MC: Music Captioning, SD: Speech Description, SED: Sound Event Detection.
Dataset
Samples
Type
Annotation
Preprocessing for Each Sample
Clotho
3,839
AC
Human
Select longest caption
AudioCaps
91,254
AC
Human
No preprocessing
CompA-R
198,648
AQA
Pipeline
No preprocessing
MusicCaps
2,649
MC
Human
No preprocessing
LP-MusicCaps
15,626
MC
Pipeline
Use summary-style caption
SpeechCraft
228,944
SD
Pipeline
Select 3-30s clips
TACOS
10,358
SED
Human
Convert tags with timestamps to caption
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AIl Sound
dd Music
What mechanical sound clearly
What best describes the emotional tone and tempo of the music?
prominent in the audio?
Engine revving repeatedly
slow piece with upbeat melodies
Airplane taking off
fast tune with cheerful singing
Matorcycle passing by
A dramatic song with loud chords
Car horn blaring
slow piece with sorrowful singing
What vocal quality does the speaker use when delivering the line?
When does the archestra finish playing the epic symphony?
fone normal pitch
21.2s
Tense tone with uneven pitch
24.4s
A loud tone with rising pitch
27. 9s
soft tone low pitch
18.65
Speech
Temporal
Figure 2: Randomly sampled questions from four distinct question types.
for AudioCaps and Clotho, "Please describe this music in detail" for MusicCaps and LP LP-MusicCaps. "Please describe this speech in detail" for SpeechCraft, and "Please identify and describe all sound events" for TACOS. For CompA-R, the identity transformation /CompA (as, (i ct) + (at, 4i Os) applied, as the original question-answar pairs are preserved without modification.
2.2 MULTIPLE CHOICE QUESTION CONSTRUCTION
To construct diverse question-answer pairs with varied formulations, transformation g Dunified DMco is applied using Qwen3-235B (Yang al. 2025a). For each tuple (ai, Qi, (4) E D Dunifeds the
function generates:
g(a; 4s 44) = (a,, of , O,, y/s 4:),
where gew represents the newly constructed question, c, is the preserved audio content description, O = {or 02, 03, 34} denotes the set of four answer options with three distractors and one correct answer, yi 21,2, 3, 4} indicates the index of the correct answer, and t; E represents the question type classification.
For datasets Diexible (Clotho, AudioCaps, CompA-R, MusicCaps, LP LP-MusicCaps, SpeechCraft), which do not contain event timestamps but provide longer captions, the question type space is de- fined Tnexible (Sound, Music, Speech} preserve data diversity. The model automatically determines the appropriate question type based on g.eT and O, with one question-answer pair gener- ated per audio sample. For TACOS, the question type space is constrained to TTACOS = [Temporal), focusing on temporal-related questions (e.g., event sequences, timestamps), and the number of gen- erated question- answer pairs per audio sample varies from to 4, determined by the model.
2.3 CHAIN-OF-THOUGIN GENERATION
Previous studies show that CoT improves language model performance. To support CoT research for LALMs, we introduce a structured CoT generation component. three-stage CoT generation function h DMcq + Deundidate is defined, where Daandidate represents the dataset at the final stage be- fore quality inspection. For each multiple choice question tuple (a,, g.eh ,0, O,, Ys, 4),, the function generates:
h(a, o0th C., Os, y/. t2) (a,,9. g.e. Or,, Ot,yi, t4, R,, Riiplle)
(2)
where R₂ = (r1s, P2s, Pss) represents the structured three-stage reasoning process:
ri; QuestiorTYypeAAnllyssss"""""" (t),
(3)
T2: AudiooContem/Aaallss(ss...
(4)
133 AnswerSelection((1:: W:).
(5)
To benefit research more efficient reasoning, simplification function R - Rsimple is then applicd using Qwen3-235B, converting the three-stage structured CoT R into natural language un- structured CoT psimple where [/simlel] < |R] in terms of reasoning chain length.

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Published as a conference paper at ICLR 2026
The generated candidate dataset is thus defined
D candidatte = {(as.98 0,'h Di, 1, R,, R2 Re )===:
(6)
where M represents the total number of generated multiple choice questions.
2.4 QUALITY CONTROL AND FILTERING
To ensure high-quality audio-based questions, each sample {a, g.th Oi, Yis ti, R, (t Deandiaate is sent to Qwen3-235B for automatic evaluation across five quality dimensions: answer consistency, distractor quality, language fluency, reasoning logic, and simplified reasoning quality. Each dimension is scored on a 5-point scale. Samples where any of the five scores falls below 4 are filtered out, resulting in the final dataset Diaal constituting the AudioMCQ dataset with 571, 118 data samples. The question type distribution and source data distribution of AudioMCQ are presented in Figure (a) and (b)) respectively. Our analysis reveals that speech-related questions consti- tute the largest proportion 47.0% (268,299 samples), followed by sound-based questions at 39.1 1% (223,126 samples). Music-related questions account for 8.1% (46,373 samples), while temporal questions represent 5.8% (33,320 samples). Regarding the source data distribution, SpeechCraft is the largest contributor, providing 39.9% (228,033 samples) of the total dataset. CompA-R follows the second-largest source, contributing 34.5% (197,218 samples). AudioCaps accounts for 15.8% (90,549 samples), while TACOS, LP-MusicCaps,, Clotho, and MusicCaps contribute 5.8% (33,320 samples), 2.7% (15,560 samples), 0.7% (3,801 samples), and 0.5% (2,637 samples), respectively.
)
Temporal 5.8% (31,320)
Music 8.1% (46,373)
39.9%
(228,033)
(197,218)
30
Percentage
Speech 47.0% (268,299)
20
15.8%
(91,549)
39.1% (223,126)
10
(33,320)
5.8%
2.7%
(15,560)
0.7% (3,801)
0.5%
(2.637)
SpeechCraft
CompA-R
AndiaCaps
TACOS
LP.MusisCapp
Clatho
Music Caps
(a) Question type distribution.
(b) Source data distribution.
Figure 3: Distribution analysis of AudioMCQ dataset.
3 WHEN LALMS SKIP AUDIO: EXPLORING AUDIO-CONTRIBUTION
3.1 DEFINITION OF AUDIO-CONTRIBUTTON
We define audio-contribution as the degree to which audio features contribute to LALM's ability to correctly answer a question within a given audio-question pair. Formally, for multiple-choice question with audio a, question 9,, options O, and ground truth answer yı. let (a, : 4t O,) denote the model prediction when the audio, question, and options are provided, and ((0, gi O,) represent the prediction using only textual information with 30-second silent audio 0 replacing the original audio. Unlike MMAU or RUListening (Zang et al. 2025) which uses Gaussian noise as replacement, silent audio to isolate textual reasoning. The audio-contribation AC(ai, qi, O,) is defined
AC(a,, (is O,) = [j,(a, qs O,) = vil - IÍ, 4. O,) = we],
(7)
where I[[]] is the indicator function that returns 1 if the condition is true and otherwise. Conversely, we introduce the concept of zero audio-contribution, which occurs when AC qı, O,) This indicates that LALMS produce identical predictions regardless of audio availability:
(a;: gi. O2) O(0, qr Ot)-
(8)
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Table 2: Performance breakdown of LALMs across audio benchmarks with silent audio input.
MMAU-test- mini Performance by Subset (%)
Subset
Qwen2-Audio
A- A-Flamingo2
RI-AQA
Kimi-Audio Owen2.5-Omii
Average
Random Guess
Sound
42.0
56.5
56.2
67.6
51.7
54.8
25.0
Music
39.8
62.3
49.7
57.2
50.9
52.0
25.0
Speech
34.5
41.4
50.5
42.9
26.7
Overall
38.8
53.4
50.0
58.4
48.5
49.8
25.5
MMAR Performance Subset (%)
Subset
Qwen2-Audio
A-Plamingo2
RI-QQA
Kimi-Audio Qwen2,5-Qmmn
Average
Random Guess
Perception
30,9
30.5
36.1
39.1
27.7
32,9
27.2
Semantic
34.5
39.6
37.6
40.5
35.4
37.5
31.4
Signal
41.9
44.2
37.2
53.5
48.8
45,1
33.0
Cultural
32.6
31.9
30.5
40.4
31.9
33,5
28.4
Overall
33.1
35.0
36.0
46.5
32.4
29.3
MMSU Performance by Subset (%)
Subset
Qwenn-Audio
A- A-Flamingo2
RI-AQA Kimi-Audio Qwen2.5-Ommt
Average
Random Guess
Perception
30.1
28.3
42.0
29.4
26.0
31.2
25.0
Reasoning
40.2
43.7
43.3
53.4
42.8
44.7
25.0
Overall 35.3
25.0
To investigate this phenomenon systematically, we analyze several mainstream open-source LALMS across three established benchmarks: MMAU-test-minn MMAR, and MMSU. Specifically, eval- uate existing LALMs on these benchmarks by replacing the audio component in each audio-question pair with silent audio input O. The detailed breakdown of this analysis is presented in Table 2 On MMAU-test-mini, models achieve an overall average of 49.8% accuracy with silent audio input, compared the random guess baseline of 25.5%. Sound-based queries show the highest accuracy with silent audio input at 54.8%, followed by music (52.0%) and speech (42.7%). MMAR demon- strates an overall accuracy of 36.6% with silent audio input, with signal processing achieving the highest accuracy at 45.1 %. On MMSU, the overall accuracy with silent audio input reaches 37.8%, with reasoning tasks showing higher performance (44.7%) than perception tasks (31.2%). Overall, all benchmarks exhibit pronounced zero audio-contribution phenomena, with MMAR and MMSU showing relatively lower accuracies with silent audio input, while MMAU-test-mini demonstrates higher accuracy with silent audio input, reaching approximately 50%.
3.2 AUDIO-CONTRIBUTIO FILTERING
Based on these preliminary insights and experimental findings on audio-contribution, we design Cndio-Contribution Filtering (ACF) to partition the AudioMCQ dataset into two distinct sub- sets: weak audio-contribution and strong audio-contribution. Specifically, three LALMs-AR Audio- Flamingo2 (A-Flamingo2)) (Ghosh et al., (025), RI-AQA, and Kimi-Audio (Ding et al. 2025 are employed to evaluate questions within AudioMCQ, with the audio component in each audio- question pair being replaced with 30 seconds of silent audio.
Let M = {M1, M2, M3} denote the set of three evaluation models, where Mi is A-Flamingo2, M2 RI-AQA, and M3 Kimi-Audio. For a given sample (a;, (i. O,, E Diiua let (O, qi, O,) denote the prediction of the model M, when provided with silent audio. We define the correctness indicator for each model as:
C, O,, v:) Ily, (0, q: O,) = y:].
If at least two out of three models can correctly answer the question under silent audio input O, the data sample is assigned to weak audio codtii-contiibttion subset D Dweaks otherwise, it is classified into strong audio-contribution subset D Danongg
Weak if Ey Ca(as O1, 2,
ACF(q, Ol, v.)
(10)
Strong otherwise,
D Dweak
{(a,, qs. Oz, 0.) ACF(q, O,, vi) = Weak],
(11)
D Darong
{aa, Gi Oz. 0.) ACF((i O,, Strong).
(12)

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Published as a conference paper at ICLR 2026
Table Performance LALMs on different audio datasets with silent audio input and the audio- contribution split ratios of these datasets. AC refers to audio-contribution.
Model Aceuracy w/o Audio (%)
Split Ratio %)
Source Dataset
Samples
A A-Famming22
RI-AQA
Kimi-Audio
Weak
Strong
Clotho
3,801
44.9
40.7
58.3
47.4
52.6
AudioCaps
90,549
41.7
38.2
59.0
44.9
55.1
CompA-R
197,218
69.8
64.7
81.6
73.5
24.5
MusicCaps
2,637
48.7
41.5
54.6
46.8
53.2
I.P-MusicCaps
15,560
47.5
41.9
60.6
49.2
50.8
SpeechCraft
228,033
35.0
47.1
58.4
45.6
54.4
TACOS
33,320
31.2
34.1
35.4
26.7
73.3
Overall
571,118
48.3
50.8
65.2
54.8
45.2
Table 4: Performance of LALMs on different audio understanding benchmarks with silent audio input and the audio-contribution split ratios of these benchmarks. AC refers audio-contribution.
Model Accuracy (%)
AC Split Ratio %
Source Dataset
Samples
A- A-Flamingo2
RI-AQA
Kimi-Audio
Weak
Strong
MMAUUttst-mini
1000
$3.4
50.0
58.4
53.9
46.1
MMAR
1000
35.0
36.0
40.5
32,9
67.1
MMSU
5000
35.8
42.7
41.0
35.7
64.3
Table 3 reveals significant variations in audio-contribution distribution across different datasets, flecting their inherent characteristics and design objectives. TACOS demonstrates the highest pro- portion of strong audio-contribution samples (73.3%), which aligns with its nature as a temporal rea- soning dataset requiring precise temporal understanding and sequential audio analysis. In contrast, CompA-R exhibits the highest proportion of weak andio-contrinuunia samples (75.5%), reflecting its focus compositional reasoning tasks. We also apply the same ACF methodology to three addi- tional audio understanding benchmarks: MMAU-Iest-mint MMAR, and MMSU. Subsequently, we refer to their strong audio-contribution subsets and MMSU-ACstrong, respectively. These filtered benchmarks provide more rigorous evalua- tion framework for assessing models' genuine audio comprehension capabilities. Table reveals significant variations in audio-contribution distribution across different benchmarks, reflecting their inherent characteristics and design objectives. MMAU-test-mini exhibits the highest proportion of weak audio-contribution samples (53.9%), while MMAR demonstrates the highest proportion of strong audio-contribution samples (67.1%).
SOMAU-test-ming Dinnstiiii-ACtoonggg MMAR MMAR-ACstrong,
3.3
CASES OF ZERO AUDIO-CONTRIBUTION
Zero audio-contribution cases notably prevalent in both constructed AudioMCQ dataset and benchmark evaluations. Therefore, we investigate the underlying causes and identify two distinct patterns. The first type. Explicit Logical Reasoning, occurs when the model can directly infer the correct answer from textual cues present in the question. The second type is Implicit Knowledge Retrieval, where the model relies on knowledge acquired during training to identify the correct op- tion despite lacking explicit textual hints. Examples of both types can be found in Appendix [D] To investigate the distribution of these types, each item is analyzed using Qwen3-235B by providing the question text, options, and the correct answer to determine whether the answer can be inferred with- out audio input. Among the 313,177 samples in the weak mndioccontribution split, 31.1% (97.364 samples) classified Explicit Logical Reasoning, of which 85.1 % (82,8) 14 samples) are derived from the CompA-R dataset designed for audio-basee reasoning, while Implicit Knowledge Retrieval comprises 68.9% (215,813 samples).

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Published as a conference paper at ICLR 2026
Table 5: Performance comparison across different models
Method
MMMAU-test-minl
MMAU
MMAR
MMSU
Reado-R
67.7
63.8
36.8
49.2
RI-AQA
68.9
68.5
50.8
61.6
Kimi-Audio
68.2
64.4
57.6
59.3
SARI
67.0
66.0
Qwen2.5-Omni (backbone)
71.5
71.0
56.7
60.6
Audio Flamingo
73.3
72.4
60.1
62.3
Omni-RI
77.0
75.0
63.4
Audio-Thinker
78.0
75.4
65.3
GPT4o-Audio
62.5
60.8
63.5
56.4
Gemini-2.0-Flast
70.5
67.0
65.6
$1.0
Our Methods
SFT
75.2
75.0
64.6
64.0
Data GRPO
78.1
75.4
63.0
70.2
Mix SFT Mix AC GRPO
74.2
74.4
69.2
Weak SFT + Strong AC GRPO
78.2
75.6
65.3
Mix Strong GRPO
76.4
67.0
71.7
4 EXPERIMENTS
We conduct series of training experiments using Qwen2.5-Omni as the backbone model to vali- date our dataset quality and training paradigms. Given our focus perceptual fidelity rather than reasoning, CoT is excluded from the following evaluations.
4.1 DATASET QUALITY VALIDATION
To validate the quality of the constructed AudioMCQ dataset, perform two experiments: SFT training and GRPO training using all data from AudioMCQ.
GRPO is novel RL approach that eliminates the need for separate value function approximation as required in PPO. Instead of using a value function as the baseline, GRPO leverages the average reward of multiple sampled outputs produced in response to the same question the baseline.
Specifically, for each question GRPO samples a group of outputs {or ... (.)) from the old policy Teas and optimizes the policy model by maximizing the following objective:
(o) F(g ~ P(Q). (0)
Ro (Ol))
molon,ly oc. Foauloura o,,a"" 11,,1++)) Avd ADxcltllannd
(13)
Σ
min
Te (oralo: o..ce) A.s. clip
where f represents the parameters of the policy model being optimized, G is the group size (number of sampled outputs per question). |o,] denotes the length of the i-th output, og, is the t-th token in the i-th output, 0i.cr represents all tokens before position in the i-th output, e is the clipping parameter, 3 is the KL divergence (van Erven & Harremos 2014) regularization coefficient, mref the reference policy, and A is the advantage calculated based on relative rewards of the outputs inside each group only.
In the experiments, the optimal checkpoint is determined based on performance on MMAU-test- mini-4k as validation. This expanded version of MMiAU-tettmmiii is created by replicating each question to ensure that the correct option appears in each position at least once. The selected check- point is subsequently evaluated MMAU-test-minn ing 2000 steps of SFT, we achieve 75.2% on MMAU-test-mint 75.0% on MMAU and 64.6% on MMAR. After conducting 1200 steps of GRPO, 78.1% % and 75.4% are achieved on MMAU-test- mini and MMAU respectively, outperforming all previous models. Additionally, 70.2% is achieved on MMSU, marking the first time this benchmark score exceeds 70%, representing a 6.2% improve- ment over the previous SFT result of 64%. These results demonstrate the effectiveness and high quality our AudioMCQ dataset.
MMAU, MMAR, and MMSU. After conduct-
PPerformance scores sourced from official benchmarks and websites when available; otherwise, reproduce results for open-source models reported scores from official papers for unreleased models.
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Published as a conference paper at ICLR 2026
4.2 SFT-TO-RE RL TRAINING PARADIGMS
Although the previous SFT--nly and RL-only experiments in sults, there is still room for improvement. A critical open question is how optimally partition data between SFT and RL stages. To address this challenge, we explore the possibility of allocating SFT and RL data based on audio-contribttion levels. We design additional SFT-to-RL experiments to in- vestigate this approach. First, baseline is established, namely Mixed-to-Mixed, which applies SFT on mixed audio-contribution data followed by GRPO on mixed audio-contribution data, with both stages randomly sampled from AudioMCQ. Subsequently, two distinct training paradigms are de- signed: Weak-to-Strong. which employs SFT on weak audio-contribution data followed by GRPO on strong audio-contribution data, and Mixed-to-Strone which uses mixed audio-contribution data SFT followed by strong cunto-conttibution data GRPO. To ensure fair comparison, all experiments maintain consistent data volumes with 3 313,177 samples for SFT, non-overlapping SFT/GRPO data. The optimal checkpoint selection based on MiMAU-test-miii--ee performance, shown in Figure
demonnstate promising re-
Week SPT Rose
MMAU-leat-minn Performance
Mixed SFT Rose
76
74
Mitul-a-X
72
Mixed--n-Strng (RL)
Strong (SFT)
Stonn (Chosen)
70
Weak-to Strong (RL)
Mixed so Mixed (RL)
Woak-tn-Shong (Choss)
Minel-a-Mincl (Chosen)
68
500
1000
1500
2000
0
500
1000 Training Steps
1500
2000
Training Steps
Figure 4: Performance comparison of three training approaches on MMAU-test-mini-4K for optimal checkpoint selection. Note that "Mixed-to-X (SFT)" indicates the shared SFT phase of Mixed-to- Mixed and Mixed-to-Strong approaches.
UMAU-test-mma Performance
MMA Milteet--mmiii--ttnnngn Performance
62-
Score
75
teall,toStuong (SFT)
Wask-te-Strnng (SFT)
Wealete-Siongg (GRPO)
Wolksto-Strang (ERPP)
Minod-to-X (SFT)
Mixed-t-XX (SFT)
Mixed to Stoong (GRPO)
58
Mixed to Strong (GRPO)
Mo (GRPO)
Miked---Miisdd (GRPO)
500
1000
1500
2000
500
1000
1500
2000
MMAR Performance
MMAR-AC AAMoR-CCttoon mance
65
60-
Weak to Stong (SIT)
Weak to Strong (SFT)
60
Weak (GRPP)
55
Woak-to Streng (GRPO)
Misul-tt-X (SFT)
Misel-ts-X (SFT)
Minsi-toSSmmn (GRPP))
Mitus-t--Ston (GRPO)
55
Mined to Mised (GRPO)
50
Mited to Mixed (GRPO)
500
1000
1500
2000
500
1000
1500
2000
MMSU mer
MMSU Cstrong Performance
70.0
64
Scare
Waak-tn-Sming (SPT)
Wask-to-Strang (SFT)
Weak to Strong (GRPO)
62
Wask to Strong (GRPO)
Mined-to (SFT)
Miked-to-X
Mined to Strong (GRPP)
60
Mixed to Strong (GRPO)
65.0
Mixed-to Mixed (GRPO)
Miued-t--Mied (GRPO)
500
1000
1500
2000
500
1000
1500
2000
Training Steps
Training Steps
Figure 5: Performance comparison of three training approaches across MMAU-test-mint MMAR, MMSU and their strong audio-contribttion splits. Only the optimal checkpoints are displayed.
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The baseline Mixed-to-Mixed approach (1000 steps SFT + 400 steps GRPO with mixed audio- contribution data) achieves 64.9% and 69.2% on MMAR and MMSU respectively, demonstrating suboptimal MMAU performance. In contrast, the Weak-to-Strong paradigm (750 steps weak audio- contribution SFT + 100 steps strong audio-contribution GRPO) surpasses the previous 1200-step GRPO-only approach, achieving SOTA performance with 78.2% on MMAU-test-mini and 75.6% on MMAU, while obtaining 65.3% on MMAR. The Mixed-to-Strong approach (1000 steps mixed audio-contribution SFT + 500 steps strong audio-contribution GRPO) attains SOTA performance on MMAR and MMSU (67.0% and 71 71.7% respectively).
Although both approaches- Weak-to-Strong and siixed-to-trron--sinniii significantly outperform the Mixed-to-Mixed baseline, understanding where these methods enhance model capabilities and when to select between them remains important. Therefore, the performance of all three meth- ods is evaluated at three training stages (initial, post-SFT, and post-GRPP) across six benchmarks (MMAU-test-minn) MMAR, MMSU, and their respective strong audio-contribution splits), with re- sults shown in Figure 5 Two key conclusions are derived from this analysis. First, using strong audio-contribution data for RL is important, as GRPO with mixed audio-contribution data yields results in degradation of audio- bbsiid question-
minimal performance improvement and can answering capability on the AC-strong splits of MMAR and MMSU. In contrast, GRPO with strong audio-contribution data yields significant improvements across all benchmarks and their AC-strong splits, reflecting a substantial gain in the model's s perception capability, particularly pronounced on the strong audio-contribution benchmarks (MMAR and MMSU). Second, SFT data selection should align with specific downstream task characteristics. Since approximately half of MMAU-test-mini questions can be answered without audio input, it represents a relatively weak audio-contribution benchmark, making weak contr-contiibution data during SFT training better matched to the bench- mark distribution. For MMAR and MMSU, which are relatively strong audio-contribution bench- marks, naturally distributed mixed audio-contribution data for SFT training proves more suitable.
5 CONCLUSION
We first present AudioMCQ, a comprehensive high-quality audio multiple-choice question dataset comprising 571k samples with structured and unstructured CoT annotations for LALMS post- training. Through systematic investigation, the zero audio-contribution phenomenon is identified, where current LALMs frequently derive correct answers from textual information alone with silent audio imput, with rates reaching 49.8% on MMAU, 36.6% on MMAR, and 37.8% on MMSU bench- marks. Audio-Contribution Filtering methodology is developed subsets based on whether question is correctly
partition the data into weak
(54.8%) and strong (45.2%) audio-contributio answered by more than half of the models under silent audio input conditions. Within weak audio- contribution data, two distinct patterns are identified: Explicit Logical Reasoning (31.1%) and Im- plicit Knowledge Retrieval (68.99 (88.%)) Based on these insights, two effective post-training paradigms developed that achieve SOTA performance among open-source models. The Weak-to-2 to-Strong approach attains 78.2% on MMAU-test-me 67.0% on MMAR and 71.7% on MMSU. The methodology demonstrates that RL with strong audio-contributtion data is important for capability enhancement, while SFT data selection should be aligned with downstream task characteristies. These contributions establish new benchmarks for LALM post-training and provide practical guidance for data allocation strategies. Future work could focus on establishing more robust cittoo-oonriiution-beeee tending
and 75.6% on MMAU, while Mixed-to-Stronp reaches
data partitioning for AudioMCQ, ex- Filtering to general audio question- aanwwiinn tasks, and further inves- tigating CoT reasoning capabilities audio understanding contexts.
6 ACKNOWLEDGEMENTS
This work was supported by the NSFC Young Scientists Fund (Category C) [grant number 62501512], the Ant Group Research Intern Program, and the Engineering and Physical Sciences Research Council (EPSRC) [grant numbers EP/T01975//1, EP/Y028805/I).
For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence any Author Accepted Manuseript version arising.
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Published as a conference paper at ICLR 2026
7 ETHICS STATEMENT
We have no ethical concerns to declare regarding this work.
8 REPRODUCIBILITY STATEMENT
We have made efforts to ensure the reproducibility of our work. Implementation details, hyperpa- rameters, and evaluation protocols are provided in the main paper and Appendix.
REFERENCES
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A USE OF LARGE LANGUAGE MODELS
Large Language Models are used solely as part of the AudioMCQ creation pipeline and for polishing portions of the manuscript. All research concepts, methodology, experimental design, analysis, and conclusions represent original work by the authors.
B PROMPTS
B.I MULTIPLE-CHOIC QUESTION GENERATION (SOUND, MUSIC AND SPEECH)
You are test designer for advanced audfo comprehens exans. Your task is to generate pulttipeechhice question BASED by Vistening to the aud io.
existing question-answse pair,
understanding gained
Here 1s the source Information:
ORIGINAL QUESTION: (extext)
ORTGINAL ANSWER: [answer)
Use Uhe following six requirements create 350N output:
REQUIREMENT QUESTION TYPE
10 Choose ONE of: "sound", "nus ic", or "speech" based on what the new questtion tar gets. 11
REQUIREMENT
QUESTION
TLe a new quest ton that: Eniss with question nark 15 Is clear, concise, specifíc
to be understoodke
that definitely
ORIGTNAL
17
Focus on vaudio- dependennt understand ing- that cannot be inferred from text alone
18
REQUIREMENT MULTIPLE-CHOICE OPTIONS
options incorrect options
correct answer),
words. Guidelines:
21 Use capital letters only at the star t, no end end punctuation
22 All four options must maintain consistent levels of detail
23 AlL four optiens must follow the options
structure grannar type
appear equally plausible All four options must have CONSISTENT word count to prevent identifying correct answers by length
tially atlentive
25
26
27 REQUIREMENT CORRECT ANSWER DESIGN
correct answer must:
29
Require sactual listening to the audios to answer
30 Be based on content that def initely rexists in the ORIGINAL ANSWER+
Extract only phrases from the original essential information while onitting secondary details REPHRASE the content us ing synoryms or parallet expressions
34
35
REQUIREMENT DISTRACTOR DESIGN
incorrect oation should:
37
Reflect plausible misunderstandings or shallow interpretations
38
Natch the correct answer's is structure, complexity, and vocabulary level the TOPIC/ASPECT the correct Have consistent length gramnatical structure correct answer
naintain thenatic consistency
41 Be clear ly distinct from the correct answer
42
REQUIREMENT VALIDATION CHECK
Ensure:
45 No answer significantly longer or nore detailed
46 No answer stands out due to word choice, phrasing, or tone
The questton-answer pair comprahenston of pudio, logic
question cannot
by language
inout
19
50 IMPORTANT:
these Return JSON object with
53
inaw_question,
qyy type": "question type",
54
nee__qusstion:: "Your new question here?"
"carract_
"Correct
here",
"incorrect cepprrecttpttiost::
57
"First incorrect option".
58
"Second incorrect option",
"third incorrect option"
]
61 ]]
02
extra explanation
6 Include trailing comnas
66 use quotation marks inconsistentlly
Nake
noticeably differant in Length
60 CRUCIAL: Your output must be indistinguishahle to a reader (or AT) who has nat heard the audio. Only someone
who Listened carefully should be able to answer corractly.
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Published as a conference paper at ICLR 2026
B.2 MULTIPLE- CHOICE QUESTION GENERATION (TEMPORAL)
You are test designer for advanced sudio comprehension exans. Your task is to generate new multiple-cheice question BASED ON existing cuestion-anawee dasigned understanding TEMPORAL INFORMATION can
gained by listening
Hore is the source information:
ORIGINAL QUESTION: (q_text) ORIGINAL
(answer)
Use the following six requirenents to create your 25ON output:
QUESTION
10 Always use "tenporal" as the question type.
11
REQUIREMENT euestien
QUESTION
Ends with a question nark
15 Is clear and concise with no more than 28 words
internal punctuation long INFORMATION 18 Clearly distingutshes temporal events using sequence indicators or appropriate adjectiieesaadvers when the audio contains other sinilar events Cannot ba
welt-formed
specific
definitely texists ORIGINAL
by
knowledge, world knowledge, logic alone
21 REQUTRENENT 3: MULTIPLE-CHOICE OPTIONS
22 write 4 options (3 incorract options and correct answer). Guidelines: ending punctuation options
follow same sentence
25 If time values are present, all four options must have DISTINCT time values <at least (randon_secondss)s
apart from each other present, Al tine values in the
If
exceed
28 REQUTREMENT 4: CORRECT ANSWER DESIGN
29 The carrect answer must:
Require Aactual listening the audios
temporal information
Be based TENPORAL INFORMATION definitely #exists in the ORIGINAL ANSWER*
33 REQUIREMENT DISTRACTOR DESIGN
Fach PLAUSTBLE incorrect temporal information
should:
36 Have cons istent length and grametical structure as the correct r answer
37 Show time differences proportional both the audio's total length and the correct answer's value
REQUIREMENT VALIDATION CHECK
40 Ensure:
No question answer pair comprehen of Lemporal information in Togic
significantly longer
detailed
question cannot solved by language without audio input
44
45
IMPORTANT:
35ON
keys:
47
{
48
tre__qeettion fon type": "terporal",
frew_question": "Your question here?",
Lanswrr"; "Correct answer here",
"incorrect options":
52 "First incorrect option".
53 "Second incerrect opt option",
"Third
option"
]
56 }}
extra explanstion
80
Include trailing connas
61 quotation inconsistentlyy
noticeably different Length
61 CRUCIAL: Your output must be indistinguishable to a reader (or AI) who has not heard the audio. Only someone who Listened carefully the Lemporal sequence should be able correctly.
66
(style_reference)
B.3 CHAIN-OF-THOUGH GENERATION (STRUCTURED)
TASK: Complete following THINKING
denonstrates at correct answer
audio-based multiple-chotce question.
2.
QUESTION DETATLS:
Question: [Kentry("questiin text"])"
Question Type:
typs
Choices: (", "join[["""[ppt]] for in entry(Lauutt_cchote
correct
"(entry[" (tntryy(annserr))"
TNCOMPLETE THINKING PROCESS:
According to the question text, question_Lyee
the question type is (entryl'
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Published as a conference paper at ICLR 2026
firstly analyze
fentry[ "'ooriiinalaansser""
14
According the audio content, http://llyypp....//////oomm////a the "[entry(" answer 13"".
17
COMPLETION REQULREMENTS:
(Question Analysts):**
20
21 1.1 Style Instructions:
Naxinun 30 words
24 Write as one CONTINUOUS paragraph without breaks
25 Be analytical and nathodical Nointain COMERENCE with the surrounding context
28 1.2 Content Instructions:
29 1. Identify the key information being asked for find Connect question requirenents
Clartfy ovidence you
audio
31 vADo not mention any information in question type
33
SECOND THINKING PROCESS (Audio Analysis
Selection):Ax
36 2.1 Style Instructions:
37
Naxinun 30 words
for first
one CONTINIOUS paragraph without 40 Naintain COMERENCE with the surreunding context
41
Use "quotes" +only when reforencting specific answer choices*, not for quoting audio content directly
44 1. Highlight the key evidence fran the audio that directly answers the question
45 2. Explain hew this evidence clearly supports the correct answer
other avaftable options logical connection 48 5. +ABO not mantion any sound details or content that are NOT nentioned in the literal content of "[entryl"
Briefly corpare
evidence answer audfo
determine they incorrect.
original_ ansserr ">".**
OUTPUT FORMAT:
51
First thinking process between <first analysis> and c/tirst analysts> tags
52
Second thrnking process between analyst_ and </second analysis>> tags
B.4 CHAIN-OF-THOUGHT GENERATION (UNSTRUCTURED)
ing process
multiple question.
QUESTION DETAILS:
Question: "(entry iquesttonn text']3"
Question Type: ceuttryluuettionttyee typee
Choices: (", (jein(("""(opt]" for opt in entry("'mlti
7
Audio Description: Satryll'anee "])"
Correct Answer: "[entryl"
ORIGINAL THINKING PROCESS:
11
centry-get(" get("thinking_pocees '})
REQUIREMENTS
VERSION:
14
15 1. <tLengthve: Maximun 150 words total
*Structurevs: Write
continuous paragraph without breaks
vAContentsA;
18
Briefly identify the questian type
19
Sufficiently introduce and sumerize salll of the Sound Eventra and Axtheir related information in
Fluent language
Briefly
carrect answer
21
No need to explain how to eliminate wrong answers, only focus on how to select the correct answer
22
.AStyleA+:
absolutely Use simple, clear language Facus on the essential reasoning only
Use ENGLISH
characters allowed
25
26
=+šelf-containedi:: Content nust complete and understandable without relying the ORIGINAL THINKING
28
Use uquuttation marks when referencing answer choices
29
Avoid using expressions Like "Event 1", "Event 2", etc.
OUTPUT FORMAT:
32
Your response must be in the following fornat: <thinking process>
33
[Your simplified thinking
here]
</thinking process>
37 Generate the sinolified thinking process:
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B.5 QUALITY CONTROL AND FILTERING
TASK: Quality chack audio-based multiple-choice questian entry five spacific aspects.
3. ENTRY TO FVALUATE:
4. Question: "[question_toxe)"
Nultiple Choice Options:
Correct Answer: "[answer)"
8. Original Audio Description: perriginalaaswerr"
Thinking Process: Sarnkiing_processe
11
12
Simple Thinking (thinking__ort pecpepprocessssee sinplee
15
16
EVALUATION CRITERIA:
Please evaluate this entry the following five aspects, scoring from 1-5:
19 *APPECT 1: Language Fluency (1-S)at
30
Evaluate the overall language quality, grarmar, clarity, and fluency of:
Question
22
Multiple choice options
23
Correct answer
24
Thinking
Simple thinking process
27
SCORING GUIDE:
28
Score 5 (Excellent)<s: Perfect grannar, crystal clear expression, natural and fluent Language throughout components. Professional quality writing.
29
*<Soore (Good)*e; Ninor gramaatccsl issues or slightly awkoard phrasing, but overall clear and understandable. Good quality writing.
30
**Score (Average)*s; Sore gramattcaa generally compreter ehreenieseee Acceplable quality. ++ocore (Poor)*e: Multiple gramatical errors, unclear or confusing expressions, unnatural language that significantly ingacts comprehension.
unclear expressions that
confusion, but
32
**Score (Very Foor)**: Severe gramatical problens, incomprehens ible highly confusing language, extrenely poor expression quality.
34
*ASPECT 2: Answer Cons istenyy (1-5)<s
35
Evaluate how well the correct answer sligns with the information provided the original audio description.
37
SCORING GUIDE:
38
s*oorr (Excellent)<<: The correct answer perfectly natches and is strongly supported by the original audio descr iption. Complete aliganent. scoree (Sood)an;
correct answer well-suporttdd by the description with only discrepancies, Strong alignnent.
40 S*ooe 3 (Average)*s; The correct enswer is generally consistent with the aud description but may have unclear connections. Moderate alignnent. z*Score (Poor)*s;
correct answer significant inconsistencies clear support. Weak alignment, 4*Score 1 (Very Foor) The correct answer directly contradicts the audio description or is completely unsupported
description
42
alignment.
44 *AAPEECT 3: Incorrect Options quality (1-5)
45
Evaluate how well the incorrect options are designed they should NOT appear in be supported by the original audia description. Incorrect Options evaluate: snnooreet_options_eettttttt
47
48
SCORING GUIDE:
options clearly contradict completely unsupported by audio éescription. They are obviously wrong and serve as perfect distractors.
**Score (Excellent)<x:;
51
**Score (Good)ax; Nost incorrect options contradict the audio description, with only minor elements that plaustble. Good distraccors overall.
nay have 53 - (Roar)as; Nany incorrect options are partially supported or appear in the audio description, naking then patentially correct. Peor distractors.
**Score (#verage)<s; Sone incorrect options contradict or unclear relationships to it. Mixed quality distractors.
description while
*vScore (Very Foor)**: Nost or all incorrect options are clearly supported by audio description, making then appear correct. Terrible distractors that confuse the question.
55
**SPECT 4: Thinking Process Logic Fidelity (1-5)++
Evaluate both logical coherence
thinking process AND whether it stays complotely faichful original sudio description without introducing fabricsted details.
58
SCORING GUIDE:
s*oor (Excellent)vv: Perfect logical quastion, austo analysis, and correct answer The thinking process is highly cohorent, well-structured, AND stays complataly faithful to the original audio description with no additional detariss or fabricated content introduced. Perfect adherence s+Sco (Good)es; Strong logical reasoning minor gaps or slightly unclear transitions. The thinking process is nostly faithful to the audia description with only
clear, sound reasoning seanlessly connects
excellent logie.
clear connactions between components, though nay have
niner, well-supported inferences. Good logic with mintnal risk of halluctnation. Score (Averagoj**: Generally logical reasoning with or unclear steps interpretations that stay generally within the bounds af the audio description. Acceptable logic with some ninor speculation.
clear connections, thinking process
may noticeable gaps some reasonable
reasoming precess.
reasoning, poor connections qon, audie analysis, and answer, thinking process introduces several detafls facts
(Poor)*e: logical with significant gaps,
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present in audio description, or significant assumptions not supported source Poor logic or notable hallucination 1ssues.
64 xeScore 1 (Very Poor)*s; Illogical or incoherent reasoning with nejor flaws, contradictions, or complete the thinking directly contradicts or goes beyond what's audio description.
failure to properly imagined content, information Severe logic problens or iallucinetion issues.
the
contains extensive fabricated details,
66
**ASPECT 5: Simple Thinking Process Legic Fidelity (1-5)<4 logical coherence the original audio description without introducing fabricated details.
Evaluate
sinple thinking process
stays complately
68
SCORING GUIDE:
clear, sound reasoning seanlessly connects question, audio anslysis, and correct answer. The simple thinking process is highly coherent, sett-structured, AND stays completely faithful the original audio description with no adcitional details or fabricated content introduced. Perfect adherence
scorre (Excellent) Perfect logical
Score (Sood)es; Strong logical reasoning naterial with excellent
connactions between components, though nay minor gaps or slightly unclear transitions. The simple thinking process is nostly faithful to the auáio description with only very minor, well-supparted inferences, Good logic with nininal risk of hallucination. with clear connections, Score (Average)a*; Generally logical simple thinking process reasoning precess.
may noticeable reasonable
unclear steps interpretations that stay generally within the bounds of the audto description. Acceptable logic with sone minor speculation.
connections between (Foor)*s: Meak logical flow with significant gaps, unclear reasoning, several details question, audie analysis, not present in the audio description, or nakes signtficant assumptions not supported by the source natorial, Poor logic notable hallucination issues.
answer,
simple thinking process
Score (Very fonr)*e: Illogical incoherant reasoning najor components.
contradictions, complte sinple thinking process contains extansive
failure to properly connact details, imagined content, or information that directly contradicts or goes far beyond what's in the audio description. Severe logic problems or halluctnation 1ssues.
OUTPUT FORMAT:
77 You must provide exactly five scares in the following format: cospectl Sarti_scoreeK/// saaraex//ssactl_scoo _ccore>
78
caspect? score>
<aspect3_ score> score>
81 caspecta_ storr>////spacca_.c... score>
82
caspects Score>ss/aspects _ score>
Y, W, integers
to
35
86 IMPORTANT:
single integer not include any explanations or add'tional outside score tags 89 Focus on objective evaluation based on the criterta provided
B.6 MODEL EVALUATION PROMPTS
Ao Flaningo 2 [Question] (A) Optionl. (B) Option2. (C) Option3. (D) Optione.
v R1 AQA
[Question] Please choose the fron the following options: ['Option2', 'Option2', '0ption'', 'Options') answer canswer> c/answer>,
Output
7 e Kimi-Audio and mudio-Reasoner
[Question] A. Optien1 6. Optioon2 Option3 Option!
B.7 MODEL TRAINING PROMPTS (QWEN2.5-OMNI)
SFT
[question] Please choose the answer fron the Following options: A. Optionl B. Option2 c. Option3 D. Opt1on4
dthers
[Question] Please choose answer from following options: L'Option2', 'Optfon2', 'Option'', 'Optfon4") J.
Output the final answer in <onswer> </answer>.
System Pronpt
are an understanding
answers nultiple questions content.
17

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Published as a conference paper at ICLR 2026
C QUALITY VERIFICATION WITHIN THE PIPELINE
The AudioMCQ construction pipeline incorporates a fine-grained, multi-step quality control process to ensure data integrity:
Automated Validation: All generated data points must pass a custom format checker for immediate structural compliance.
2. Iterative LLM Correction:
Data failing the initial format check automatically sent back to the Large Language Model for re-generation until the required format is met.
3. Manual Spot-Checking: Batches of data undergo quality assurance via manual review by
a team of 3 human annotators.
4. System Refinement: Issues identified during manual review necessitate modifications to the LLM prompting strategy and checker rules, followed by full re-generation of the corresponding dataset stage implement systemic corrections.
D EXAMPLES OF TWo CASES OF ZERO AUDIO-CONTRIBUTION
D.1 EXPLICIT LOGICAL REASONING
Q: What mechanical sound is continuously present in the background?
A. The revving motorcycle
B. The running engine (correct)
C. The passing train
The screeching brakes
The question asks: "What mechanical sound is continuously present back- ground?" The key word in the question is "continuously." which implics a sustained, ongoing sound rather than an intermittent or brief one. Now, examine the answer choices in light of this clue: "The revving motor- cycle" Revving is typically intermittent, involving bursts of sound, not continuous. "The running engine" engine that running produces steady, continuous sound, matching the description of "continu- ously present." "The passing train" A train passing by produces sound that increases and then decreases in volume; it is temporary and not continuous. "The screeching brakes" Screeching hrakes are usually short-lived and abrupt, not continuous. Based solely on the textual clucs especially the word "continu- ously"-w can logically infer that "The running engine" is the only option that describes mechanical sound that would be consistently present background. The other options describe transient or momen- tary sounds. Therefore, the correct answer can be determined from the text alone. without needing to hear the audio.
Qwen3-235h Analysis:
Q: What specific sound do the car's tires make as it accelerates? A. They squeal slightly while moving fast (correct) They screech loudly while stopping
They crunch over gravel rapidly
D. They hum quietly on the highway
Qwen3-235B Analysis: The question asks about the specific sound the car's tires make as accelerates. The correct answer is "They squeal slightly while moving fast." Looking at the answer choices, each describes different tire sound associated with specific driving condition: "They squeal slightly while moving fast" implies acceleration with some tire grip stress; "They screech loudly while stopping" refers to braking, not accelerating: "They crunch over gravel rapidly" suggests driving on a gravel surface; "They hum quietly on the highway" indicates steady-speed driving. The key clue in the question is "as it accelerates." which directly relates to the moment the increases speed. Among the options, only "squeal slightly while moving fast" plausibly corresponds tire sound during acceleration, increased traction demand when speeding up. The other choices are tied to different actions (braking, driving on gravel, cruising), which are inconsistent with acceleration. Therefore, using only the textual clues particularly "accelerates" - and matching it to the most contextually appropriate sound from the choices, deduce the correct answer without hearing the audio.
tire squeal often occurs due
Q: What type of vehicle can be heard accelerating quickly?
A. A car starting slowly
B. A train approaching
bicycle moving quietly
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Published as a conference paper at ICLR 2026
D. A motorcycle speeding away (correct)
Qwen3-235h Analysis: The question asks: "What type of vehicle can be heard accelerating quickly?" The key clue in text is the phrase "accelerating quickly," which implies vehicle that produces noticeable and rapid increase in speed, typically associated with a loud or sudden engine sound. Now, examining the answer choices: "A car starting slowly" contradicts "accelerating quickly" due to "slowly." "A train approaching" while trains can be loud, "approaching" does not necessarily imply acceleration, and trains are less commonly described as "accelerating quickly" in everyday auditory contexts. "A bicycle moving quictly" - "quietly" contradicts the idea of being clcarly heard accelcrating; also, bicycles do not have engines and typically don't produce loud acceleration sounds. "A motorcycle speeding away" - "speeeding away" directly aligns with "accelerating quickly," and motorcycles are commonly associated with rapid acceleration and loud engine noises. All necessary information to eliminate incorrect choices and select the correct one is present in the textual description. The phrase "accelerating quickly" provides strong textual clue that, when matched with the descriptors in the answer choices, allows identification of "A motorcycle speeding away" as the most logically consistent option aption-without without needing to hear the audio.
Q: What mechanical auditory pattern occurs continuously throughout the audio?
A. A siren fluctuating in pitch periodically
B. A machine starting and stopping abruptly
C. The sound of wind increasing and decreasing
D. An engine maintaining steady operation (correct)
Qwen3-235B Analysis: The question asks: "What mechanical auditory pattern occurs continuously throughout the audio?" The key word in the question is "continuously." which implics that the correct answer must describe sound that does not stop or fluctuate significantly- must be unbroken and steady over time. Now, examine the answer choices in light of the word "continuously"; "A siren fluctuating in pitch periodically" The word "periodically" indicates repeating change, meaning the sound is steady or continuous in pitch; it varies. This contradicts a truly continuous pattern. machine starting and stop- ping abruptly" "SStatiin and stopping" clearly indicates an intermittent pattern, not a continuous one. "The sound of wind increasing and decreasing" "Increasing and decreasing" suggests variation over time, not steady, continuous state. "An engine maintaining steady operation" "Maintaining steady operation" di- rectly implies consistent, unchanging, and continuous mechanical sound. Given that the question explicitly uses the word "continuously." and only one choice describes truly continuous and unchanging mechanical auditory pattern, the correct answer can be deduced from the textual clues alone, without needing to hear the audio.
Q: What mechanical sound remains constant throughout the audio?
A. A horn beeping
engine running steadily (correct)
C. A motor turning on and off'
D. A siren approaching quickly
The question asks: "What mechanical sound remains constant throughout the au- dio?" The key phrase in the question is "remains constant throughout the audio." This implics that the correct answer must describe a sound that unchanging or continuous nature. Now, evaluating the an- swer choices based on this clue: "A car homn beeping" This is intermittent, not constant. "An engine running steadily" - The word "steadily" directly indicates a constant, unchanging sound. "A motor turning and off" This is cyclical and not constant. "A siren approaching quickly" This involves changing pitch (Doppler effect) and is constant. Even without hearing the audio, the phrase "remains constant" allows us to logically deduce that "An engine running steadily" the only option that matches the descrip- tion of continuous, unchanging mechanical sound. The textual clues in the question and the semantics of the answer choices are sufficient to identify the correct answer.
Qwen3-235B Analysis:
D.2 IMPLICIT KNOWLEDGE RETRIEVAL
Q: What specific actions are clearly heard in the audio?
A. Ducks quacking and liquid splashing (correct)
B. Birds chirping and wind blowing
Ducks flying and thunder rumbling
D. Frogs croaking and water dripping
Qwen3-235B Analysis: The question asks: "What specific actions are elearly heard in the audio?" This
implies that the answer depends on auditory content - specifically, which sounds are perceptible in the audio. The answer choices all descriptions of soundscapes (e.g.. "ducks quacking and liquid splashing", "birds chirping and wind blowing"), "), and the correct answer is stated to be "Ducks quacking and liquid
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splashing". However, the task is determine whether this question be answered **based solely textual clues the question** **, without listening the audio. The question itself does not contain any explicit textual indicators about what is heard there are no descriptions the sounds, no metaphors, no contextual hints (such as a setting like "a pond during storm") ) that would allow reader to infer which sounds are present. All the answer choices descriptive context in the question text, there is way determine which is correct based alone. The identification of the correct answer relies entirely on hearing the audio or having a textual description of its contents, which is not provided. Therefore, the correct answer cannot be determined from the text alone.
plausible in certain environments, but without additional
Q: What is the sequence of audio elements at the beginning?
A. Steam hissing followed by speech (correct)
B. Music playing followed by speech
C. Footsteps approaching followed by speech
Wind blowing followed by speech
The question asks about the "sequence of audio elements at the beginning." which inherently refers to the temporal order of sounds in an audio clip. The answer choices describe specific sound events (e.g.. "steam hissing," "music playing") followed by speech, which are auditory in nature. The question text itself does not provide any explicit description of the audio content, such "the audio starts with steam hissing" question text, is impossible to determine the correct sequence of audio elements hased solely on the text. Therefore, one must rely on listening to the audio to identify which sound comes first and whether it is followed by speech. Since the textual clues in the question are insufficient to determine the correct answer, the audio content is necessary.
Qwen3-235B Analysis:
any equivalent textual clue. Without such deseriptive information emhedded in the
Q: What occurs while female speaker is continuously talking?
A. A door slams shut loudly
B. A spraying sound happens several times (correct)
C. A dog barks intermittently
D. A phone rings in the distance
The question asks: "What occurs while the female speaker continuously talk- ing?" This implies that the correct answer must describe an event that happens simultaneously with the female speaker's continuous speech. The correct answer is "A spraying sound happens several times." To determine whether this can be answered using only the textual clues, we must assess if the question itself contains enough information deduce the correct answer without hearing the audio. However, the question text does not mention any details about spraying sound, a door, a dog, or phone. There are no descrip- tive hints, onomatopoeia, or contextual clues (e.g., setting, activity, or associated actions like gardening or a phone call) that would allow one to infer that spraying sound occurs. All answer choices are auditory events that could plausibly occur in the background, but nothing in the text favors over the others based logic or implication. Therefore, without the audio, it impossible determine which sound occurs during the female speaker's 's continuous talking. The text of the question alone does not provide sufficient clues to arrive at the correct answer.
Qwen3-235B Analysis:
Q: What specific sound event occurs audio?
A. A motorcycle increases speed moving away (correct)
B. A car engine idling steadily
C. A motorcycle decreases speed approaching
D. A bicycle speeding on pavement
"What specific sound occurs in the audio?" and provides four answer choices describing different sound events involving vehicles. The correct answer stated to be "A motorcycle increases speed moving away." However, the question text itself does not contain any descriptive clues about what is happening in the hints, narrative descriptions that would allow correct answer depends entirely on interpreting the auditory characteristies (such as the Doppler effect, engine pitch changes, etc.) present in the audio, which are not described or hinted at in the text. Since the textual information provided in the question does not include any clues to determine the sound event without listening to the audio, the correct answer cannot be deduced from the text alone.
Owen3-235B Analysis: The question asks:
are onomatopoeic words, contextual infer the sound event. The identification of the
Q: What combination natural and animal sounds present the audio?
Wind blowing with insect buzzing and bird chirping (correct)
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Published as a conference paper at ICLR 2026
B. Ocean waves with seagull crying and boat horn
Leaves rustling with dog barking and footsteps
D. Rain falling with frog croaking and thunder rumbling
Qwen3-235B Analysis: The question asks about the specific combination of natural and animal sounds present in an audio file. The answer choices provide different combinations of environmental and animal sounds, such wind, insects, birds, occan waves, scagulls, frogs, etc. However, the question text itself does not include any explicit description or textual clue about what sounds are actually in the audio. It only frames the query and offers multiple choices. There is no additional context, transcript, or onomatopocic hints within the question text that would allow one to deduce the correct combination without hearing the audio. Therefore, it is not possible to determine the correct answer using only the textual clues in the question. Listening the audio is necessary identify the actual sounds present.
E EXPERIMENT DETAILS
E.1 HYPERPARAMETER CONFIGURATION
We present the detailed hyperparameters used our experiments. All experiments were conducted using the same hardware setup to ensure fair comparison.
Table 6: Hyperparameters for SFT.
Parameter
Value
Training Type
Full Parameter bfloat16
Data Type
Max Sequence Length
1024
Training Epochs
2
Batch Size (per device)
20
Learning Rate
lc-6
Gradient Accumulation Steps
Warmup Ratio
0.05
LR Scheduler
Cosine
Optimization
DeepSpeed ZeRO-2
Table 7: Hyperparameters for GRPO.
Parameter
Value
Training Type
Full Parameter
Data Type
bfloat16
Max Completion Length
1024
Training Steps
1000
Batch Size (per device)
8
Learning Rate
1e-6
Warmup Ratio
0.05
Number of Generations
8
Temperature
1.5
Top-k Sampling
4
KL Divergence Penalty (8)
0.001
LR Scheduler
Cosine
Optimization
DeepSpeed ZeRO-2
E.2 EXPERIMENTAL CONTROLS FOR SFT-TO-RL PIPELINE
To ensure the reliability and validity of our experimental results, we implement strict controls across multiple dimensions:
The SFT training data volume is fixed 313,177 samples, corre- sponding exactly to the low contribtnniiiioonn split size in AudioMCQ, ensuring consistent comparison across different experimental conditions.
Fixed Data Volume:
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Data Augmentation: During training, each multiple-choice question is replicated four times with randomized option orders to reduce position bias.
Data Isolation: SFT and RL training datasets are kept strictly non-overlapping to prevent data leakage and ensure that performance improvements can be attributed solely to the RL training methodology.
SFT Model Selection: During SFT. we evaluate checkpoints at three intermediate steps (750, 1000, and 1250) and select the best- rest-performing model based on MMAU-test-miii-4k evaluation scores to mitigate training variance effects.
RL Training Protocol: The RL phase consists of exactly 1000 training steps across all periments, with final checkpoint selection based on comprehensive performance evaluation MMMAUttes--miii-4K
E.3 PERFORMANCE OF OUR METHODS
Table 8: Detailed performance of methods. Model A: All Data SFT; Model B: All Data GRPO;
Model C: Mix AC SFT Mix AC GRPO; Model D: Weak AC SFT + Strong AC GRPO; Model E: Mix AC SFT + Strong AC GRPO.
MMAU-test-mini Performance by Subset (%)
Subset
Model A
Model
Model C
Model D
Model E
Average
Random Guess
Sound
79.6
82.6
76.0
83.8
79.3
80.3
25.0
Music
72.5
74,9
71.6
72.2
72.8
72.8
25.0
Speech
73.6
76.9
75.1
78.7
77.2
76.3
26.7
Overall
75.2
78.1
74.2
78.2
76.4
76.4
25.5
MMAU Performance by Subset (%)
Subset
Model A
Model
Model C
Model D)
Model E
Average
Random Guess
Sound
78.2
80.3
76.4
79.0
78.6
78.5
Music
72.2
68.8
70.6
71.7
70.4
70.7
Speech
74.6
77.0
76.1
76.3
76.5
76.1
Overall
75.0
75.4
74.4
75.6
75.1
75.1
MMAR Performance by Subset (%)
Subset
Model
Model
Model
Model D
Model
Average
Random Guess
Perception
61.4
64.1
64.4
64.1
68.6
64.5
27.2
Semantic
67.5
64.3
66.8
67.5
67.0
66.6
31.4
Signal
67.4
48.8
55.8
60.5
60.5
58.6
33.0
Cultural
64.5
60.3
63.8
63.8
64.5
63.4
28.4
Overall
64.6
63.0
64.9
65.3
67.0
65.0
29.3
MMSU Performance by Subset ( (%)
Subset
Model A
Model
Model C
Model D
Model E
Average
Random Guess
Perception
49.8
60.4
61.7
60.1
64.2
59.2
25.0
Reasoning
79.2
80.7
77.2
79.3
79.8
79.2
25.0
Overall
64.0
70.2
69.2
69.3
71.7
68.9
25.0
22


Reviews:
[{"review_id": "fTXOR2j0fV", "reviewer": "Reviewer_xxSA", "summary": "The paper studies the post-training strategies for training large audio language models. The paper explores multistage post-training strategies such as RL followed by SFT, instead of single-stage post-training. The paper uses the multi-stage post-training to improve the zero audio-contribution phenomenon in LALMs, where models derive correct answers solely from textual information without processing audio content. The paper proposes a comprehensive MCQ dataset to facilitate large-scale post-training.", "questions": "The experiments for the two proposed post-training paradigms, Weak-to-Strong and Mixed-to-Strong, are poorly designed. Why is the number of training steps across the three training experiments different? \n\nLine 442: “Mixed-to-Mixed approach (1000 steps SFT + 400 steps GRPO with mixed audio contribution data)”\n“Weak-to-Strong paradigm (750 steps weak audio-contribution SFT + 100 steps strong audio-contribution GRPO)” \n“Mixed-to-Strong approach (1000 steps mixed audio-contribution SFT + 900 steps strong audio-contribution GRPO)” \nThis makes drawing conclusions about the post-training strategies difficult. Is the “Mixed-to-Strong” strategy better than the “Mixed-to-Mixed” strategy because of how data is used, or is it simply due to the larger number of training steps used for the “Mixed-to-Strong” strategy?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The paper is well-written and explores important yet underexplored multistage post-training strategies. The proposed dataset is comprehensive and focuses on designing MCQ that require the LALM to pay attention to audio to answer correctly. The paper achieves strong performance across several benchmarks, such as MMAU and MMAR.", "weaknesses": "The synthetic data generation, followed by post training, has been used before to improve performance of prior models such as R1-Qwen and R1-Omni.\n\nThe performance improvement across the two benchmarks is very small; for example, on the MMAU test and test-mini benchmark, the performance is just 0.2, and on MMAR is <2 (Table 5), despite using significantly more data for post-training than the existing methods, such as Omni-R1.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AB6SQPQayi", "reviewer": "Reviewer_YhaS", "summary": "The paper presents AudioMCQ, a comprehensive audio multiple-choice question dataset comprising 571k samples with two kinds of chain-of-thought annotations and investigates the prevalent zero audio-contribution phenomenon in LALMs, where models derive correct answers solely from textual information without processing audio content. To address this, the authors propose an Audio-Contribution Filtering method to partition data into 'weak' and 'strong' subsets. Based on this, they introduce effective post-training paradigms, such as Weak-to-Strong, which achieve new state-of-the-art performance on benchmarks like MMAU, MMAR, and MMSU.", "questions": "1. Regarding Section 2.4, you use Qwen3-235B to automatically evaluate samples. Did you consider using a hold-out evaluator from a completely different model family (e.g., GPT-4, Gemini, Claude) to validate this filtering process? Using the same model for generation and evaluation risks introducing a significant confirmation bias.\n2. Your ACF method creates a binary weak/strong partition. Audio contribution, however, likely exists on a spectrum. Could you comment on the limitations of this hard binary split? Have you considered an alternative approach, such as a \"soft\" assignment where samples are weighted by their predicted audio-contribution strength during training?\n3. You provide CoT annotations to \"support CoT research\" but do not use them for training. Do you have a hypothesis on how CoT fine-tuning would interact with your weak-to-strong paradigm? For instance, would you expect CoT to be more beneficial when applied to the 'strong' audio-contribution data, where complex, multi-step reasoning about audio events is presumably required?\n4. The results show that the optimal SFT data strategy (Weak-AC vs. Mixed-AC) depends on the audio-contribution characteristics of the downstream benchmark. This suggests the primary benefit might be a distribution matching effect (i.e., aligning the SFT data distribution with the test distribution). How would you distinguish this effect from the more fundamental claim that your paradigm teaches the model a generalizable, robust reasoning process? Is it more about domain adaptation than creating a more capable model overall?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "* The paper is well written.\n* The dataset collection process is very thoroughly explained. \n* The quantification of this \"zero audio-contribution\" scenario (e.g., 49.8% average accuracy on MMAU-test-mini with silent audio) is very relevant to the area.\n* The experiment setup is complete and shows the benefits of the proposed post-training paradigms.\n* The paper achieves new and significant state-of-the-art results on several diverse and challenging audio understanding benchmarks (MMAU, MMAR, MMSU).", "weaknesses": "1. There are no experiments with the CoT data collected from the dataset. These experiments would not only \"further investigate the CoT reasoning capabilities\" of models but actually solidify the contribution of the dataset in this end. Without this experiment, it is hard to evaluate if these CoT traces' quality.\n2. The entire Audio-Contribution Filtering (ACF) method hinges on a majority vote from three specific LALMs. This choice is not justified, ablated, or analyzed for sensitivity. It is unclear how robust the resulting \"weak\" and \"strong\" data partitions are. A different set of filtering models could potentially lead to a different partition, which would call into question the generality of the training paradigms built upon them.\n3. The AudioMCQ dataset was generated and filtered using a single powerful LLM (Qwen3-235B), and the experiments are conducted using a model from the same family (Qwen2.5-Omni). This alignment could mean the model has an inherent advantage on this specific data distribution, potentially inflating the reported performance gains. I would be interesting to see the benefit of this training data and splits on a different base model.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aA3VDLftBt", "reviewer": "Reviewer_DR3o", "summary": "This paper investigates whether audio contributes to answering audio-based questions and the authors create an audio Q&A dataset and discover that models often answer correctly using just the text context, ignoring the audio. They split the training data into cases where audio is actually needed vs. not, and then train the model in stages, focusing on the audio-critical examples. This targeted training leads to better performance on audio question answering tasks.", "questions": "- How can the audio-contribution-aware approach be applied to more open-ended or dialog-style audio question answering tasks that don’t have predefined options? \n- The dataset provides detailed CoT reasoning for each question. However, the paper does not isolate how much these reasoning prompts actually improve model performance or reasoning ability. Would the LALM perform significantly worse without the CoT training data, or do the benefits mainly come from the RL fine-tuning and audio-centric data filtering?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper introduces AudioMCQ, a large-scale audio multiple-choice question dataset with 571,000+ samples.\n- Identification of zero audio-contribution issues: The authors systematically measure how often models answer correctly without using the audio. By replacing audio, they find that current LALMs still achieve far-above-chance accuracy.\n- They propose ACF to split training data based on whether the audio actually affects correctness, with data partitioning, allowing the training to focus differently on audio-easy vs audio-critical examples.", "weaknesses": "- All experiments and the dataset use a multiple-choice Q&A setup. The method’s effectiveness on open-ended or free-form audio questions remains untested.\n- The ACF procedure uses predictions from three existing LALMs to decide if audio is needed for each sample, this automatic labeling might be noisy or biased.\n- The two training paradigms showed different strengths across benchmarks, and no single approach uniformly excels. For example, the Weak-to-Strong method achieved the best results on the MMAU benchmark but underperformed on datasets where audio cues were crucial, where Mixed-to-Strong worked better. This indicates the training strategy must be aligned to the task’s audio-contribution type, which further adds complexity.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WeP62TQsZJ", "reviewer": "Reviewer_dnN2", "summary": "This paper presents a good motivation and an interesting problem formulation, but the proposed approach relies heavily on previous works and employs a relatively simple fusion method, which limits its novelty.", "questions": "Regarding the frequency-domain knowledge extraction, what is the highest frequency captured? Does this differ between general audio and speech when extracting meaningful features? It would be helpful if the authors could compare these aspects in experiments and share their insights with the research community.\n\nAs this paper seeks to advance large audio-language models through a newly proposed dataset, could the authors clarify how frequently the dataset has been utilized in the domain and provide evidence that it is well-defined and meaningful? Have you released the dataset and codes, or do you plan to make them publicly available?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The problem formulation for “evidence bottleneck” is very intriguing and inspiring for the poor acoustic grounding. The paper is well-written with clear motivations and methodology formulation. Prioritizing the perceptual front-end is a novel and reasonable approach to enhancing the audio understanding ability of LALMs.", "weaknesses": "Since the work builds upon pretrained models such as Whisper, CED-Base, and Kimi-Audio-7B, the network design itself is not particularly innovative, as much of the credit should be attributed to these existing models. The main contribution lies in effectively combining and fusing them using LoRA. This direction is promising and could be further strengthened to reach the level of ICLR.\n\nThe Time-Aware Alignment and Inject-and-Add Fusion components are not novel, as similar operations are commonly used in speech and audio processing. Please clarify what specific novelty, if any, is introduced in these parts.\n\nAlthough GRPO is a known term, please provide its full name for clarity.\n\nThe experimental evaluation appears limited, given that it includes comparisons with only five LALMs and three benchmarks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)