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in the models’ paramet- ric memory (Mallen et al., 2023), making contex- tual information more crucial. For smaller models (Llama 3.2 3B, Llama 3.1 8B, and Qwen 2.5 7B), PopQA exhibits stronger positional effects when rotating the relevant passage. The effect is less pronounced in Llama 3.3 70B due to its larger para- ... | https://arxiv.org/abs/2505.15561v1 |
We compute the distracting effect on Qwen 2.5 7B. 1 5 10 15 20 25 k0.20.40.60.8HITS@kBGE+RR BGE BM25+RR BM25 (a) 1 5 10 15 20 25 k0.20.40.6Precision@kBGE+RR BGE BM25+RR BM25 (b) 1 5 10 15 20 25 k0.20.40.60.8MaxDE@kBGE+RR BGE BM25+RR BM25 (c) 1 5 10 15 20 25 k0.140.160.180.200.220.240.26MeanDE@kBGE+RR BGE BM25+RR BM25 (... | https://arxiv.org/abs/2505.15561v1 |
3: [ 'Honeysuckle Rose '] Document 3: (Title: On the Road Again (Willie Nelson song)) The song , about life on tour , came about when the executive producer of the film Honeysuckle Rose approached Nelson about writing the song for the film 's soundtrack . ''On the Road Again ''became Nelson 's 9th Country & Western No.... | https://arxiv.org/abs/2505.15561v1 |
is just a popular misconception. ``` These documents are all IRRELEVANT because: - They lack the necessary information to support any of the acceptable answers, even though they may contain some related information. - They reference similar themes, keywords, or surrounding context but don 't provide the specific answer... | https://arxiv.org/abs/2505.15561v1 |
They do not contain any information that contradicts the acceptable answers. - Only semantic meaning matters; capitalization, punctuation, grammar, and order don 't matter. - Hedging and guessing are permissible, provided that at least one of the acceptable answers is fully included and the response contains no incorre... | https://arxiv.org/abs/2505.15561v1 |
effects of data age, domain coverage, quality, & toxicity win at NAACL '24?", the acceptable answer is "Outstanding Paper Award". The predicted answer "Outstanding Paper" would be considered CORRECT, because "award" is presumed in the question. - For the question "What is the height of Jason Wei in meters?", the accept... | https://arxiv.org/abs/2505.15561v1 |
results for Llama 3.2 3B across datasets. 1 2 3 4 5 6 7 8 910 Relevant Passage Position7275788184879093Accuracy NQ 1 Relevant + 4 Weak Distracting 1 Relevant + 9 Weak Distracting 1 2 3 4 5 6 7 8 910 Relevant Passage Position7275788184879093 TriviaQA 1 2 3 4 5 6 7 8 910 Relevant Passage Position7275788184879093 PopQA 1 ... | https://arxiv.org/abs/2505.15561v1 |
Olympics in St. Louis, 1932 Summer Olympics and 1984 Summer Olympics in Los Angeles; and the 1996 Summer Olympics in Atlanta; BULLET::::- the 1932 Winter Olympics and 1980 Winter Olympics in Lake Placid, New York; the 1960 Winter Olympics in Squaw Valley, California; and the 2002 Winter Olympics in Salt Lake City, Utah... | https://arxiv.org/abs/2505.15561v1 |
arXiv:2505.15563v1 [cs.CL] 21 May 2025Semantic-based Unsupervised Framing Analysis (SUFA): A Novel Approach for Computational Framing Analysis Mohammad Ali College of Information University of Maryland College Park, USA mali24@umd.edu aliusacomm@gmail.comNaeemul Hassan Philip Merrill College of Journalism College of In... | https://arxiv.org/abs/2505.15563v1 |
it is important to understand how news media outlets frame the issue, as media framing determines how people “choose to act upon [the problem]” [Entman 1993, p. 54]. Study 1 examines how individual words, such as adjectives and adverbs, convey different meanings related to the shooter, victims, and the shooting event. ... | https://arxiv.org/abs/2505.15563v1 |
revolves around two broad competing as- pects: emphasis framing and equivalency framing. Equivalency framing involves presenting two or more alternatives with logically equivalent phrases (e.g., loss versus gain) Kahneman and Tversky [1984]; Levin et al .[1998]. In contrast, emphasis framing refers to the act of repeat... | https://arxiv.org/abs/2505.15563v1 |
as adjectives (e.g., young shooter), verbs (e.g., shooter kills), or even participles and modal verbs (e.g., shooter accused of [killing]). In qualitative textual and quantitative content analyses that rely on manual labor, scholars might code the keyword “warlord,” keep- ing other parts (e.g., noun and semantic relati... | https://arxiv.org/abs/2505.15563v1 |
bag-of-words- based practices, such as topic modeling. This limitation calls for exploring an unsupervised technique to capture semantic relations among words for better identifying frames. This article intends to fill the gap by focusing on unsupervised methods of framing analysis. Although a few studies attempted to ... | https://arxiv.org/abs/2505.15563v1 |
questions beginning with "why?" It deals with the information they use in making causal inferences, and with what they do with this information to answer causal ques- tions. (p. 107). As naïve psychologists, people tend to make two broad types of causal attributions: a) dispositional attributions and b) situational att... | https://arxiv.org/abs/2505.15563v1 |
the United States, Joe Biden, visited Texas to console the victims and pledged to act [Liv- ingston 2022; The Biden White House Achieve 2022]. Within a month of the Uvalde school mass shooting that occurred 10 days after another shooting in Buffalo, New York, a gun safety legisla- tion was passed by the Senate and Cong... | https://arxiv.org/abs/2505.15563v1 |
2022. Of the news outlets, The New York Times (NYT) and Cable News Network (CNN) are selected as the left-leaning news media, and the Wall Street Journal (WSJ) and Fox News as the right-leaning news media [Check nd]. The news media outlets were categorized based on their bias scores provided by Media Bias/Fact Check (M... | https://arxiv.org/abs/2505.15563v1 |
the shooting incident, in certain ways. Each type of word and phrase was coded into a separate code. For instance, the words “kills, ” “killing,” and “killed” were coded into a single code. In the axial coding phase, where related codes are grouped into broader categories, we organized initial codes into similar catego... | https://arxiv.org/abs/2505.15563v1 |
used the word “slaughter” to describe the incident, while Fox News and the WSJ did not. The left-leaning media also used “terrorist attack,” which was not used by their right-leaning counterparts. In contrast, right- leaning media outlets identified the shooting as a “senseless crime. ” The use of words such as “terror... | https://arxiv.org/abs/2505.15563v1 |
particular meanings to construct frames. For instance, in the excerpt “An 18-year-old gun- man on Tuesday fatally shot 19 children and two adults” from an NYT article, the semantic relation between the phrase “18-year-old” and the “gunman” (Salvador) highlights that the modifier refers to the gunman and not the childre... | https://arxiv.org/abs/2505.15563v1 |
1). Manual data analysis enables the researchers to identify such se- mantic relations and relevant frames, as presented above. Therefore, semantic relations appeared essential for having relevant meanings and frames in a text. In a computational method, being able to capture the semantic relations seems to be a one-st... | https://arxiv.org/abs/2505.15563v1 |
three entities, the shooter, victims, and the event, we needed to identify and resolve the coreferences (e.g., “he” or “suspect” for the shooter) to capture all possible modifying words of both “references” and “co-references.” To accomplish this, we applied NeuralCoref, an extension of the spaCy NLP library that provi... | https://arxiv.org/abs/2505.15563v1 |
outperformed them. The clusters revealed through k-means clustering were not found to be coherent and adequately insightful for understanding the nuances of frames, as we examined the results manually. The findings of the qualitative analysis reveal that right-leaning and left-leaning news media outlets use different w... | https://arxiv.org/abs/2505.15563v1 |
tragic (1), and fatally [shot] (1), which frames the issue as a more significant problem. Such words used by right-leaning outlets include deadly (4), deadliest (3), awful (4), horrific (3), senseless (2), and devastated (1). This shows the right-leaning outlets use less intense words like “senseless” and “awful,” whic... | https://arxiv.org/abs/2505.15563v1 |
right-leaning ones sympathize with the shooter, as evidenced above. 8 Semantic-based Unsupervised Framing Analysis (SUFA): A Novel Approach for Computational Framing Analysis AEJMC, August 07–10, 2023, Washington, DC 13 Integrated Discussion of Both Studies This research’s primary objective is to introduce and explore ... | https://arxiv.org/abs/2505.15563v1 |
appropriate as SUFA is meant to analyze large datasets. However, if the size of modifying wordsis small enough to manage manually, a qualitative analysis might be more suitable for step 7, as research suggests that human intelli- gence often outperforms machines in tasks that require contextual interpretation and subje... | https://arxiv.org/abs/2505.15563v1 |
headline and body texts, and exemplars. Such advancements will provide a more comprehensive understanding of framing effects in news media. References Mohammad Ali and Naeemul Hassan. 2022. A survey of computational framing analysis approaches. In Proceedings of the 2022 conference on empirical methods in natural langu... | https://arxiv.org/abs/2505.15563v1 |
4 (1993), 51–58. Gail Fairhurst and Robert Sarr. 1996. The art of framing . San Francisco: Jossey-Bass. Gail T Fairhurst. 2005. Reframing the art of framing: Problems and prospects for leadership. Leadership 1, 2 (2005), 165–185. William A Gamson and Andre Modigliani. 1989. Media discourse and public opinion on nuclear... | https://arxiv.org/abs/2505.15563v1 |
and Robert McKeever. 2022. Gun violence as a public health issue: Media advocacy, framing and implications for communication. Newspaper research journal 43, 2 (2022), 138–154. Saul McLeod. 2010. Attribution theory . https://www.simplypsychology.org/attribution- theory.html Simply Psychology. Ad Fontes Media. n.d.. . ht... | https://arxiv.org/abs/2505.15563v1 |
syntacting analysis. In Proceedings of the 5th Annual ACM Web Science Conference . 423–430. Teun A Van Dijk. 1985. Structures of news in the press. Discourse and communication: New approaches to the analysis of mass media discourse and communication 10 (1985), 69. Dror Walter and Yotam Ophir. 2019. News frame analysis:... | https://arxiv.org/abs/2505.15563v1 |
‘crime’, ‘slayings’, ‘murder’, and ‘aftermath’.’amod’, ’advmod’, ’compound’, ’nummod’, and ’relcl’ 12 Semantic-based Unsupervised Framing Analysis (SUFA): A Novel Approach for Computational Framing Analysis AEJMC, August 07–10, 2023, Washington, DC Table 3: Framing Components (with Frequencies) Deployed by Each News Me... | https://arxiv.org/abs/2505.15563v1 |
school (14); nummod: 2012 (1), 215 (1), 693 (1), two (1); relcl: killed (3), say (1), took (1) Right-center WSJadvmod: away (2); amod: awful (4), deadliest (1), horrific (1), latest (2), local (1), mass (2), new (2), next (1); compound: Mass (1), mass (20), school (8); nummod: 2011 (1), claimed (1), died (1), have (1),... | https://arxiv.org/abs/2505.15563v1 |
arXiv:2505.15585v1 [cs.IR] 21 May 2025MIRB: Mathematical Information Retrieval Benchmark Haocheng Ju1, Bin Dong,2,3,4∗ 1School of Mathematical Sciences, Peking University 2Beijing International Center for Mathematical Research and the New Cornerstone Science Laboratory, Peking University 3Center for Machine Learning Re... | https://arxiv.org/abs/2505.15585v1 |
the Evaluation Forum (CLEF) from 2020 to 2022, includes two main tasks: answer retrieval and formula retrieval, with both queries and corpora sourced from Mathematics Stack Exchange. Similarly, the NTCIR series [ 42] features a formula+keyword search task over corpora drawn from arXiv and Wikipedia. However, existing M... | https://arxiv.org/abs/2505.15585v1 |
between queries and documents. Modern approaches leverage deep neural networks to encode queries and documents into dense vectors, enabling relevance assessment based on semantic similarity. A widely adopted training paradigm for these dense retrieval models [ 27,35,31,40] involves pretraining on large- scale unsupervi... | https://arxiv.org/abs/2505.15585v1 |
labeled as duplicates on math forums like Mathematics Stack Exchange (MSE) and Math Overflow (MO). This task is inspired by the CQADupStack dataset [ 8]. A key challenge in this task is identifying semantically equivalent questions that may differ in phrasing or notation but express the 3 Table 1: Statistics of the dat... | https://arxiv.org/abs/2505.15585v1 |
is a duplicate of B and B is a duplicate of C, then A is also considered a duplicate of C. From each connected component in the graph, we randomly sample one question to serve as a query. The remaining questions constitute the initial corpus, which we further refine. To mitigate the issue of false negatives—questions t... | https://arxiv.org/abs/2505.15585v1 |
The evaluation metric is nDCG-prime, introduced in [ 30], which excludes unjudged documents from the ranked list. As a result, we adopt a dynamic corpus approach, where the corpus for each query consists only of its associated annotated documents. ProofWiki. ProofWiki is a mathematical library containing definitions, a... | https://arxiv.org/abs/2505.15585v1 |
Can you take it from here? I also need to know how would one go about rotating this about the y-axis, but have no idea where to start. The link from above also covers the formula for rotation about the y-axis. Table 5: NaturalProofs example. Query Relevant Document IfHis an open covering of a closed and bounded subset ... | https://arxiv.org/abs/2505.15585v1 |
formula’s surrounding context. This task requires a deep understanding of the semantic meaning of mathematical formulas. We evaluate this task using two datasets: NTCIR-12 Wikipedia Formula Browsing (WFB) [42] and ARQMath-Task 2 [45, 21, 20]. NTCIR-WFB. The NTCIR-12 Wikipedia Formula Browsing task involves retrieving r... | https://arxiv.org/abs/2505.15585v1 |
Mathlib4 Retrieval Given a mathematical query, retrieve relevant theorems. MSE Dup. Question RetrievalGiven a math question, retrieve questions that are duplicates of the given oneMO Dup. Question Retrieval ARQMath-Task-1 Given a math problem, retrieve its solution. ProofWikiGiven a math theorem, retrieve its proof.Sta... | https://arxiv.org/abs/2505.15585v1 |
40.68 UAE-Large-V1 40.43 41.11 67.44 31.66 54.81 28.17 27.85 4.64 5.59 30.17 71.92 55.50 38.27 bge-large-en-v1.5 41.99 41.70 67.40 31.02 56.36 30.25 27.53 5.45 6.84 30.51 73.76 55.22 39.00 Open-source models (>1B) gte-Qwen2-1.5B-instruct 55.17 43.13 67.73 41.97 77.83 52.56 27.46 8.40 18.64 28.05 72.96 53.56 45.62 e5-mi... | https://arxiv.org/abs/2505.15585v1 |
52.29 / 46.76 / 48.74 50.36 / 45.30 / 47.11 49.93 / 44.67 / 46.55 54.54 / 48.36 / 50.42 2_R, NV-Embed-v2, GritLM-7B and text-embedding-3-large, to assess whether reranking improves performance. The results, shown in Table 10, indicate that reranking generally leads to a decline in performance. In a few cases, slight im... | https://arxiv.org/abs/2505.15585v1 |
Science , pages 625–635. Springer, 2021. [4]Leonardo Mendonça de Moura, Soonho Kong, Jeremy Avigad, Floris van Doorn, and Jakob von Raumer. The Lean Theorem Prover (System Description). In Amy P. Felty and Aart Middeldorp, editors, Automated Deduction - CADE-25 - 25th International Conference on Automated Deduction, Be... | https://arxiv.org/abs/2505.15585v1 |
and Retrieval. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 6766–6805, Bangkok, Thailand, August 2024. Association for Computational Linguistics. [15] Chankyu Lee, Rajarshi Roy, Mengyao ... | https://arxiv.org/abs/2505.15585v1 |
[27] Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, Johannes Heidecke, Pranav Shyam, Boris Power, Tyna Eloundou Nekoul, Girish Sastry, Gretchen Krueger, David Schnurr, Felipe Petroski Such, Kenny Hsu, Madeleine Thompson, Ta... | https://arxiv.org/abs/2505.15585v1 |
and Jian-Yun Nie. C-Pack: Packed Resources For General Chinese Embeddings. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , SIGIR ’24, page 641–649, New York, NY , USA, 2024. Association for Computing Machinery. [41] Kaiyu Yang, Aidan M Swope, Alex Gu,... | https://arxiv.org/abs/2505.15585v1 |
Yang, Yifan Song, Wenhao Wu, Furu Wei, and Sujian Li. LongEmbed: Extending Embedding Models for Long Context Retrieval. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen, editors, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages 802–816, Miami, Florida, USA, November 202... | https://arxiv.org/abs/2505.15585v1 |
nonempty open subspaces U⊂Xand V⊂Ywhich are isomorphic as algebraic spaces over S.Assume XandYare birational. Let f:U→ Yandg:V→Xdefine inverse dominant rational maps from XtoYand from Yto X. After shrinking Uwe may assume f: U→Yfactors through V. Asg◦fis the identity as a dominant rational map, we see that the composit... | https://arxiv.org/abs/2505.15585v1 |
arXiv:2505.15607v1 [cs.CL] 21 May 2025From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning David Dinucu-Jianu∗1Jakub Macina∗1,2Nico Daheim1,3 Ido Hakimi1,2Iryna Gurevych3Mrinmaya Sachan1 1Department of Computer Science, ETH Zurich2ETH AI Center 3Ubiquitous Knowledge... | https://arxiv.org/abs/2505.15607v1 |
continue. Just one more step. I will encourage the student. The correct answer should be 4. I got the answer x = 6 My first step: 5x-3-2x=9Walk me through your steps. Good! What's next? We can add 3: 3x = 12 Almost there Thus the answer is: x = 4Student Pedagogical Judges Post-dialog student solve rate = Optimize Rewar... | https://arxiv.org/abs/2505.15607v1 |
example, for asking questions (Sonkar et al., 2023; Puech et al., 2025) or detecting mis- takes (Wang et al., 2024b) but it is tedious and sensitive to changes (Jurenka et al., 2024). A more robust alternative is to use gradient- based updating, for example, SFT on teacher- student dialogs. However, this is challenging... | https://arxiv.org/abs/2505.15607v1 |
(PPO) (Schulman et al., 2017) have been crucial for the success of human- preference alignment in GPT models. Direct Pref- erence Optimization (DPO) (Rafailov et al., 2023) has emerged as a simpler alternative without the re- quirement of a reward model that allows fine-tuning on offline pairwise preference data. Exten... | https://arxiv.org/abs/2505.15607v1 |
n-th token of the output sequence ut. In Section 3 we define the pedagogical princi- ples that the generated utterances should fulfill. Learning θcan then also be framed as an RL problem under the lens of Markov Decision Pro- cesses (MDP) for which we re-define the previously introduced quantities in common notation. T... | https://arxiv.org/abs/2505.15607v1 |
a penalty λ≥0which is a hyperparame- ter. The penalty gets subtracted only if any of the pedagogical judges ( rped= 0) do not accept the conversation. Intuitively, this provides a way of trading off solution correctness indicated by rsolagainst ped- agogy measured by rped. If we only care about solution correctness, we... | https://arxiv.org/abs/2505.15607v1 |
LLM judge (prompt in Figure 9). MathTutorBench (out -of-domain): We addition- ally evaluate on the independent MathTutorBench benchmark (Macina et al., 2025), which provides several automatic metrics for tutor quality. We mainly focus on those metrics that rely on the benchmark’s learned Pedagogical Reward Model (Ped-R... | https://arxiv.org/abs/2505.15607v1 |
in the held-out test set, a judge from an- other model family is used, namely, Gemma3-27B . We compare to several tutor baselines: Qwen2.5-7B-Instruct without any fine-tuning, SocraticLM (Liu et al., 2024) as a special- ized open-source tutoring model and LearnLM as a specialized close-source tutoring model, GPT-4o-202... | https://arxiv.org/abs/2505.15607v1 |
3, increasing the penalty λreduces solution leakage and improves pedagogical reward, at the cost of student success. Figure 1 shows how various settings of our frame- work trace a Pareto frontier between student learn- ing gains and pedagogy. At λ= 0.75, for instance, our Qwen2.5-7B-RL model achieves a balanced perform... | https://arxiv.org/abs/2505.15607v1 |
is fine-tuned from the Math version of Qwen, degrades performance. Similarly, supervised fine-tuning (SFT) results in decrease on math-heavy benchmarks (–7.5% on GSM8K, –9.4% on MATH500). These findings demonstrate that RL-based alignment better preserves core rea- soning skills, avoiding the trade-off between peda- go... | https://arxiv.org/abs/2505.15607v1 |
to support the community in improving LLMs at tutoring capabilities and align them with good pedagogical practice based on learning sciences. However, there are potential risks related to the reward function and reward hacking. If the reward function is redefined or an inappropriate penalty isused, the model might lear... | https://arxiv.org/abs/2505.15607v1 |
Zhi- hong Shao, Zhuoshu Li, Ziyi Gao, and 181 others. 2025. Deepseek-r1: Incentivizing reasoning capa- bility in llms via reinforcement learning. Preprint , arXiv:2501.12948. Tim Dettmers, Mike Lewis, Sam Shleifer, and Luke Zettlemoyer. 2022. 8-bit optimizers via block-wise quantization. In International Conference on ... | https://arxiv.org/abs/2505.15607v1 |
socratic personalized teach- ing with large language models. In The Thirty-eighth Annual Conference on Neural Information Process- ing Systems . Jakub Macina, Nico Daheim, Sankalan Chowdhury, Tan- may Sinha, Manu Kapur, Iryna Gurevych, and Mrin- maya Sachan. 2023a. MathDial: A dialogue tutoring dataset with rich pedago... | https://arxiv.org/abs/2505.15607v1 |
and Oleg Klimov. 2017. Proxi- mal policy optimization algorithms. arXiv preprint arXiv:1707.06347 . Lior Shani, Aviv Rosenberg, Asaf Cassel, Oran Lang, Daniele Calandriello, Avital Zipori, Hila Noga, Or- gad Keller, Bilal Piot, Idan Szpektor, Avinatan Has- sidim, Yossi Matias, and Remi Munos. 2024. Multi- turn reinforc... | https://arxiv.org/abs/2505.15607v1 |
Manling Li. 2025. Ragen: Understanding self-evolution in llm agents via multi-turn reinforcement learning. Preprint , arXiv:2504.20073. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou. 2022. Chain of thought prompt- ing elicits reasoning in large langu... | https://arxiv.org/abs/2505.15607v1 |
Llama-3.1-8B-Instruct achieves an accuracy of 25% while the tutor model Qwen2.5-7B-Instruct achieves 66% on the dataset. 1-10% 11-20% 21-30% 31-40% 41-50% 51-60% Llama-3.1-8B Solve Rate (%)010002000300040005000Number of Problems4352 2022 1802 12641319 1186Distribution of Problem Difficulties Figure 4: Distribution of p... | https://arxiv.org/abs/2505.15607v1 |
and tutor system prompts used during a conversation are in Figure 7 and Figure 8. Exact prompt for judg- ing the leakage of the solution by a teacher model is in Figure 9 and Figure 10 shows the prompt for the helpfulness of the tutor response. E Example Conversations Examples of the conversations from our model are in... | https://arxiv.org/abs/2505.15607v1 |
of the final solution too early. - Be allowed to confirm correct reasoning, correct mistakes, and clarify concepts gradually. - Clarify certain parts in more detail if the student does not get it. It is **not acceptable** for the teacher to: - Provide the full answer upfront or within the first message. - Reveal all ke... | https://arxiv.org/abs/2505.15607v1 |
issues mid-explanation.)* - **Incomplete or Empty**: Teacher sends incomplete, empty, or abruptly cut-off messages. *(Example: Teacher’s message suddenly ends mid-sentence.)* ##**Examples:** ### **Good Example (OK)**: - Student: I don’t get the second step of this solution. - Teacher: Could you tell me exactly which pa... | https://arxiv.org/abs/2505.15607v1 |
Example: Teacher guides the student without directly giving the answer. /times-circleBad Example: Teacher Solves the problem fully (Qwen2.5-7B-RL λ= 0.0) Problem: Calculate the sum of all values of xthat satisfy the equation (x2−5x+ 5)(x2+4x−60)= 1. Consider the conditions for ab= 1and provide the exact sum of valid xv... | https://arxiv.org/abs/2505.15607v1 |
square root of some number ( x) and got 9: √x= 9⇒x= 81 If they had pressed the correct key ( x2), they would have squared 81: 812= 6561 Great job understanding the problem! Figure 14: Bad Example: The model solves the entire problem directly instead of prompting the student to think through the steps. ♂lightbulbMistake... | https://arxiv.org/abs/2505.15607v1 |
Learn to Reason Efficiently with Adaptive Length-based Reward Shaping Wei Liu1∗Ruochen Zhou2Yiyun Deng1Yuzhen Huang1Junteng Liu1 Yuntian Deng3Yizhe Zhang4Junxian He1∗ 1The Hong Kong University of Science and Technology2City University of Hong Kong 3University of Waterloo4Apple Abstract Large Reasoning Models (LRMs) hav... | https://arxiv.org/abs/2505.15612v1 |
friend. Now, how many apples do I have? I count them: one, two. So, that must be two apples in total. Therefore, 1 plus 1 equals 2.Wait, is there another way to think about this? Maybe using objects or fingers. If I hold up one finger and then add another finger, how many fingers are up? That's two. Yeah, that still ma... | https://arxiv.org/abs/2505.15612v1 |
the desired reasoning length should evolve during training as the model’s reasoning behaviors dynamically change, and (2) rather than uniformly encouraging short or long CoT, length-based reward shaping should be difficulty-aware – allowing harder questions a higher token limit while constraining easier questions to fe... | https://arxiv.org/abs/2505.15612v1 |
utilize GRPO [20] to optimize this objective. RL for Efficient Reasoning In addition to enhancing reasoning capabilities, RL also holds promise for improving token efficiency in LRMs [ 11,16]. Several approaches have been proposed to this end. Most methods involve reward shaping, where models are incentivized to produc... | https://arxiv.org/abs/2505.15612v1 |
term C(y)and a length-based term S(y)controlled by a control variable λ(y): ˆR(x, y) =C(y) +λ(y)·S(y) (2) In most length reward methods, C(y) =R(x, y), representing the original rule-based reward for correctness. However, in truncation-based approaches, C(y) = 0 as we discuss below. The term S(y) denotes the length rew... | https://arxiv.org/abs/2505.15612v1 |
R(x, y),I(R) α·I(L(y)≤LT) LASER -D R(x, y),I(R) α·I(L(y)≤LA) LASER -DE R(x, y),1 α·I(R)·I(L(y)≤ LA) +α·(1−I(R))·I(L(y)> LA) Budget-based Reward Budget-based rewards use query-specific target lengths (budgets) and penalize responses that deviate from these instructions. While this mitigates reward hacking seen in group-... | https://arxiv.org/abs/2505.15612v1 |
Moreover, these target length hyperparameters are dynamically adjusted throughout training. We separate queries into three buckets of easy, medium, and hard difficulty levels, based on the correctness rates within the rollout batch – for each question, we have krollouts and use thresholds k/3and2k/3to separate them. As... | https://arxiv.org/abs/2505.15612v1 |
questions receive smaller target lengths (i.e. smaller scaling factor β), while harder questions receive larger ones (i.e. larger scaling factor β). Since monitoring runs every N steps, the difficulty-dependent target lengths are automatically adapted to the evolving policy model. Computational Efficiency This automati... | https://arxiv.org/abs/2505.15612v1 |
we only show three most representative results here. For the full results, please refer to Tabel 6 in Appendix H. Accuracy (%) Generation Length (tokens) MATH 500AIME AMCOlympiad BenchAvg.MATH 500AIME AMCOlympiad BenchAvg. Original 83.9 28.9 71.6 43.3 56.9 5042 15956 8202 11510 10177 T8192 81.8 24.8 70.9 43.9 55.3 1795... | https://arxiv.org/abs/2505.15612v1 |
-DE achieve significant improvements, demonstrating that incorporating a dynamic and difficulty-aware mechanism greatly enhances the efficacy-efficiency trade-off. Compared to other baseline methods, L ASER still exhibits a more favorable trade-off. 6.3 Experiments on Larger Models To better evaluate the effectiveness ... | https://arxiv.org/abs/2505.15612v1 |
the efficacy- efficiency trade-off on GPQA and the average performance across all benchmarks. Compared to the original model, LASER ,LASER -DandLASER -DE consistently achieve significant improvements in both accuracy and token usage, demonstrating robust generalization capabilities. And LASER -Dand LASER -DE maintain t... | https://arxiv.org/abs/2505.15612v1 |
the answer. Our analysis of MATH500 (detailed in Appendix L) reveals that the original model tends towards verbose, redundant explanations of single ideas. In contrast, the LASER -D-trained model expresses the same concepts more succinctly using structured formulas, significantly improving token efficiency. This sugges... | https://arxiv.org/abs/2505.15612v1 |
Li, N. Tian, P. Huang, P. Zhang, Q. Wang, Q. Chen, Q. Du, R. Ge, R. Zhang, R. Pan, R. Wang, R. J. Chen, R. L. Jin, R. Chen, S. Lu, S. Zhou, S. Chen, S. Ye, S. Wang, S. Yu, S. Zhou, S. Pan, S. S. Li, S. Zhou, S. Wu, S. Ye, T. Yun, T. Pei, T. Sun, T. Wang, W. Zeng, W. Zhao, W. Liu, W. Liang, W. Gao, W. Yu, W. Zhang, W. L... | https://arxiv.org/abs/2505.15612v1 |
Information Processing Systems Datasets and Benchmarks Track (Round 2) , 2021. [10] B. Hou, Y . Zhang, J. Ji, Y . Liu, K. Qian, J. Andreas, and S. Chang. Thinkprune: Pruning long chain-of- thought of llms via reinforcement learning, 2025. URL https://arxiv.org/abs/2504.01296 . [11] Kimi, A. Du, B. Gao, B. Xing, C. Jian... | https://arxiv.org/abs/2505.15612v1 |
Mays, F. Wang, F. P. Such, F. Raso, F. Leoni, F. Tsimpourlas, F. Song, F. von Lohmann, F. Sulit, G. Salmon, G. Parascandolo, G. Chabot, G. Zhao, G. Brockman, G. Leclerc, H. Salman, H. Bao, H. Sheng, H. Andrin, H. Bagherinezhad, H. Ren, H. Lightman, H. W. Chung, I. Kivlichan, I. O’Connell, I. Osband, I. C. Gilaberte, I.... | https://arxiv.org/abs/2505.15612v1 |
T. Hashimoto. Reasoning to learn from latent thoughts, 2025. URL https://arxiv.org/abs/2503.18866 . [19] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 , 2017. [20] Z. Shao, P. Wang, Q. Zhu, R. Xu, J. Song, M. Zhang, Y . Li, Y . W... | https://arxiv.org/abs/2505.15612v1 |
28.9%Original Model Truncation Group-based ThinkPrune LASER (Ours) LASER-D (Ours) LASER-DE (Ours)Model Performance on AIME2024 (b) Figure 5: Pareto-optimal trade-off between accuracy and response length across various methods. Each point represents a single training run with different hyper-parameters. Our methods, LAS... | https://arxiv.org/abs/2505.15612v1 |
while mannually 15 reducing the value of LA. Specifically, they progressively decrease LAthrough values of 4096 ,3072 , and2048 across the three stages. D.2 Group-based Rewards In the context of group-based rewards, the length reward S(y)is specifically designed to promote brevity by assigning higher scores to shorter ... | https://arxiv.org/abs/2505.15612v1 |
of 0.6, and train with a mini-batch size of 64. In our preliminary experiments, we found long-to-short RL benefits from clip-higher strategy [ 27]. So we follow DAPO [ 27] and set ϵhigh as 0.28. For evaluation, we maintain a sampling temperature of 0.6 and permit a maximum of 32,768 tokens to be generated. The number o... | https://arxiv.org/abs/2505.15612v1 |
the training process of LASER -DandLASER -DE. Figure 10 shows how the adaptive target length LAchanges over training iterations for both methods. As demonstrated in Figure 10, our method dynamically selects appropriate target lengths based on problem difficulty. For easy problems (left figure), the model quickly identi... | https://arxiv.org/abs/2505.15612v1 |
43.1 53.3 1193 4839 2457 2703 2798 L1-Max-1024 76.4 15.0 59.4 39.1 47.5 661 1303 933 938 959 L1-Max-4096 79.7 20.0 65.0 41.0 51.4 875 1718 1159 1229 1245 LASER LT=2048 83.6 29.2 71.6 44.1 57.1 1913 4815 2493 2767 2895 LASER LT=4096 83.9 31.0 74.1 45.7 58.7 1914 5915 3136 3579 3636 LASER LT=8192 85.6 31.5 75.9 47.7 60.2... | https://arxiv.org/abs/2505.15612v1 |
=−α· |L(y)−LT| where α= 0.03andLT= 10 . L1-Max C(y) = 0 λ(y) =I(R) S(y) = clip( α·(L(y)−LT) +δ,0,1) where α= 0.03andLT= 10 . LASER C(y) =R(x, y) λ(y) =I(R) S(y) =α·I(L(y)< LT) where LT= 10 . LASER -D C(y) =R(x, y) λ(y) =I(R) S(y) =α·I(L(y)< LA) where LA∈ {10,7.5,5}. 21 LASER -DE C(y) =R(x, y) λ(y) = 1 S(y) =α·I(R)·I(L(... | https://arxiv.org/abs/2505.15612v1 |
"This approach won't work because..."). (2) Verification: Systematically checking intermediate results or reasoning steps (e.g., "Let's verify this result by..."). (3) Subgoal Setting: Breaking down complex problems into smaller, manageable steps (e.g., "To solve this, we first need to..."). (4) Enumeration: Solving pr... | https://arxiv.org/abs/2505.15612v1 |
117 \] Taking the square root: \[ \sqrt{117} = 3\sqrt{13} \] Thus, the distance between the points is \(\boxed{3\sqrt{13}}\) units.<think> Okay, so I need to find the distance between two points: (2, -6) and (-4, 3). Hmm, I remember from school that there's a formula for this kind of problem. Let me think. Is it someth... | https://arxiv.org/abs/2505.15612v1 |
Square root: sqrt(117). Factor: 117 = 9 * 13, so sqrt(9*13) = 3*sqrt(13). Yeah, that all looks correct. Is there another way to approach this problem? Maybe by plotting the points on a graph and using the Pythagorean theorem? Let me think. If I plot (2, -6) and (-4, 3), the distance between them would be the hypotenuse... | https://arxiv.org/abs/2505.15612v1 |
Similarly, q is the sum from k = 1 to infinity of 1/k³, and I think that's called the Apery's constant. I don't recall its exact value, but again, maybe I don't need to compute it exactly. The main goal is to find an expression for the double sum: S = ∑_{j=1}^∞ ∑_{k=1}^∞ 1/(j + k)³. And express this S in terms of p and... | https://arxiv.org/abs/2505.15612v1 |
= 1}^\infty \sum_{k = 1}^\infty \frac{1}{(j + k)^3}\]in terms of $p$ and $q.$ [Total Tokens: 17523][Total Tokens: 1114]Figure 14: Additional case study demonstrating the evolution of reasoning efficiency. In this example, the original model required over 17K tokens to solve a question from the MATH500 dataset, while ou... | https://arxiv.org/abs/2505.15612v1 |
a fraction. So adding them up:6/3 + 5/3 + 3/3.Adding the numerators: 6 + 5 + 3 is 14, so the total is 14/3.Wait, hold on, let me double-check my calculations because 2 + 5/3 + 1 is 3 + 5/3, which is 14/3. Yeah, that seems right.But let me make sure I didn't make any mistakes in computing each f(x). Maybe I should check... | https://arxiv.org/abs/2505.15612v1 |
14 divided by 3 is 4 with a remainder of 2, so that's 4 and 2/3. But the question says to express as a common fraction, so 14/3 is fine.Alternatively, if I were to write it as a decimal, it's approximately 4.666..., but the question wants an exact fraction, so 14/3 is the way to go. I think that's thorough. I don't see... | https://arxiv.org/abs/2505.15612v1 |
arXiv:2505.15623v1 [cs.CL] 21 May 2025Can LLMs understand Math? Exploring the Pitfalls in Mathematical Reasoning Tiasa Singha Roy∗, Aditeya Baral∗, Ayush Rajesh Jhaveri, Yusuf Baig New York University {ts5478, ab12057, aj4332, yb2510}@nyu.edu Abstract Large language models (LLMs) demonstrate considerable potential in v... | https://arxiv.org/abs/2505.15623v1 |
to generate an answer, a′ ibased on the initial prompt piand question qi. For each a′ i, we create a multi-turn setup, which includes providing the agent with the correct solution to induce self-checking. We provide the LLM with the correct final answer value afito check. This approach verifies whether the final answer... | https://arxiv.org/abs/2505.15623v1 |
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