Pith. sign in

REVIEW 4 major objections 6 minor 29 references

Adaptive Preference Optimization with Uncertainty-aware Utility Anchor

T0 review · 4 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A learned, prompt-dependent utility anchor lets offline preference optimization train on unpaired winning and losing responses while keeping competitive alignment performance.

desk verdict A useful empirical method with a mis-stated theoretical claim; the anchor idea is worth a look, but Eq. (8) is a false equality that undermines the paper's main narrative. read the letter →

arxiv 2509.10515 v1 pith:YZFSBQNM submitted 2025-09-03 cs.LG cs.CL

Xiaobo Wang , Zixia Jia , Jiaqi Li , Qi Liu , Zilong Zheng This is my paper · ORCID
classification cs.LGcs.CL
keywords preferenceoptimizationutilityanchorunpaireddataBradley-TerrymodelLLMalignmentuncertaintypenaltydirectofflinelearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that the pairwise assumption in offline preference optimization, encoded by the Bradley-Terry model, is not necessary. It proposes UAPO, which replaces a single winning-versus-losing comparison with two comparisons: winning response versus a learned utility anchor, and anchor versus losing response. Because the anchor is prompt-dependent, the objective can be evaluated on unpaired preferred and dispreferred responses, so alignment training no longer requires matched pairs. The paper shows that UAPO wraps existing methods such as DPO and SimPO, improves stability under noisy preference labels and across hyperparameter settings, and preserves downstream knowledge. If correct, this would make preference data collection cheaper and alignment training more flexible.

What carries the argument

The utility anchor r(x, y_anchor) is the load-bearing object: a learnable prompt-dependent reward threshold, implemented as a linear map over the prompt's last-layer hidden states, plus a constant offset. It splits the pairwise preference signal into two sigmoid comparisons, winning-above-anchor and anchor-above-losing, which is what makes unpaired training possible. The two-term loss in Eq. (9) is the mechanism that carries the argument, and the multi-response generalization in Eq. (11) is what lets the method exploit imbalance between preferred and dispreferred responses. The pessimistic-RL interpretation of the anchor as an uncertainty penalty connects the construction to overoptimization

What would settle it

Compute, on held-out prompts, the correlation between the learned anchor and an oracle uncertainty threshold obtained from a known latent reward with controlled label noise; a weak or negative correlation would falsify the claim that the anchor estimates annotation uncertainty. A second check would replace the linear anchor with a per-prompt learned scalar and observe whether the unpaired-training benefit disappears.

Watch

Extended reading notes

Core claim

UAPO's central move is to replace the Bradley-Terry log-likelihood log sigma(r(x,y_w) - r(x,y_l)) with log sigma(r(x,y_w) - r(x,y_anchor)) + log sigma(r(x,y_anchor) - r(x,y_l)), where the anchor is a dummy token whose reward is the utility anchor. The anchor is a learnable, prompt-dependent reference point, implemented as a linear function of the prompt's last-layer hidden states, that is trained to sit between the winning reward and the losing reward. Because the two sigmoid terms separate wins from losses, the loss no longer needs the two responses to come from the same comparison; the paper derives a multi-response form in Eq. (11) that pools any number of preferred and dispreferred respo

Load-bearing premise

The load-bearing premise is that a prompt-dependent utility anchor learned by a linear function reliably estimates the uncertainty threshold between preferred and dispreferred responses; if it does not, the two sigmoid comparisons misclassify responses and the unpaired-training claim collapses.

Editorial extensions

If this is right

  • Offline preference optimization can be run on datasets with unpaired winning and losing responses, so data pipelines no longer need to force every prompt into exactly one win/lose pair.
  • Existing preference objectives can be converted by inserting an anchor; the paper demonstrates this for DPO, SimPO, and R-DPO, with the anchored variants matching or exceeding their base methods on instruction-following benchmarks.
  • The training signal becomes more stable: with 40% of win/lose labels randomly swapped, UAPO's performance drop on AlpacaEval 2 is smaller than SimPO's, and the method tolerates a wider range of the constant reward margin.
  • KL divergence to the reference policy stays lower and smoother during training, which the paper links to retaining general knowledge and mathematical reasoning on downstream tasks.
  • Because Eq. (11) pools multiple preferred and dispreferred responses, the method can use the abundant losing responses instead of discarding them, improving data utilization.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, the linear anchor is the least-tested piece: if annotation uncertainty is highly nonlinear in the prompt, a linear map from last-layer hidden states may cap the method's accuracy. A direct ablation against a small MLP anchor or a per-prompt learned threshold would settle this.
  • The anchor's role as an uncertainty penalty suggests an active-learning extension the paper does not explore: prompts whose anchor is close to the winning or losing reward are exactly the ambiguous cases worth re-labeling or sending to a stronger judge.
  • If the unpaired-data claim holds, preference data from naturally occurring signals, such as thumbs up/down, user edits, or model self-corrections, could be used directly without constructing synthetic losing responses, substantially broadening the data sources for alignment.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes Adaptive Preference Optimization with Utility Anchor (UAPO), an offline preference-optimization framework that replaces the Bradley-Terry pairwise log-likelihood with two sigmoid terms involving a learnable "utility anchor" r(x, y⊥). The anchor is intended to model annotation uncertainty and to allow training from unpaired or multiple winning/losing responses. The authors instantiate UAPO on DPO and SimPO (UAPO and SimUAPO), provide experiments on Mistral-7B, Llama-3-8B, and Gemma-2-9B across AlpacaEval 2, Arena-Hard, MT-Bench, RewardBench, and downstream tasks, and report competitive or improved performance with better robustness to hyperparameter changes and preference noise. The paper also claims a theoretical connection to pessimistic RL via an uncertainty penalty.

Significance. If the framework were rigorously established, it would be a practically useful contribution: offline preference optimization from unpaired data would improve data efficiency, and the anchor mechanism could provide robustness to label noise and distribution shift. The empirical evaluation is broad, including multiple base models, standard alignment benchmarks, OOD reward benchmarks, KL-divergence analysis, robustness to preference corruption, and downstream-task retention. The authors are also candid about MT-Bench instability and include ablations on the constant reward anchor. However, the central theoretical derivation contains a load-bearing error: Eq. (8) asserts an equality that Eq. (7) itself shows to be false, and the unpaired-data extension in Eq. (11) is not actually derived. The empirical results may hold for the surrogate objective, but the paper's main conceptual claims about equivalence to BT modeling and about the anchor's uncertainty semantics are not supported as written.

major comments (4)
  1. [§2.3, Eq. (7)–(8)] The claim that Eq. (8) is a rewrite of the BT log-likelihood is incorrect. Eq. (7) correctly establishes log σ(r_w − r_⊥) + log σ(r_⊥ − r_l) ≤ log σ(r_w − r_l), with strict inequality for every finite reward. Therefore the UAPO objective in Eq. (9) is not equivalent to BT preference modeling; it is a surrogate obtained by splitting the pairwise margin. This error is internal to the manuscript: Appendix B later explicitly treats Eq. (9) as a lower-bound optimization, while §2.3 and the abstract advertise consistency with BT-style optimization. Moreover, if Eq. (9) is interpreted as a loss, the inequality implies L_UAPO ≥ L_BT, so the paper's language about a "lower bound" is also misleading. This invalidates the theoretical grounding of the unpaired-data derivation, which relies on the "separate form" of Eq. (8).
  2. [§2.3, Eq. (11) and Appendix A] The extension to multiple winning and losing responses is not derived. Starting from Eq. (9), for a single pair one has L_w = −log σ(r(x,y_w) − r(x,y_⊥)). For multiple winners, the natural softmax-type generalization would place each winning response in a denominator with the anchor and all other candidates; the formula in Eq. (11) instead sums over all winning responses in the denominator of each term, which double-counts the target response and is not shown to follow from Eq. (9). The single L_l term is also asymmetric. Appendix A simply states the generalization without a proof. Since learning from unpaired data is a central advertised contribution, this step needs a correct, explicit derivation or a clearly stated modeling assumption.
  3. [§3.3, Figure 1] The interpretation of Figure 1(a) as showing that the utility anchor "effectively classifies" responses is circular. The loss in Eq. (9) is minimized by making σ(r_w − r_⊥) and σ(r_⊥ − r_l) large, i.e., by pushing the anchor between winning and losing rewards. That the anchor lies between them during training is therefore a consequence of the objective, not an empirical discovery about the anchor's learned representation. The stronger claim that the anchor captures prompt-dependent annotation uncertainty is an assumption that is not tested. Appendix C implements the anchor as a linear function of last-layer hidden states, but no ablation against other parameterizations (e.g., MLP, scalar, or non-parametric anchor) is reported, so the uncertainty-aware interpretation is under-supported.
  4. [§2.4, Eq. (13)–(15)] The theoretical analysis connecting UAPO to pessimistic RL is not rigorous. Eq. (14) writes a gradient with respect to π_θ while treating u_ϕ as both a fixed penalty and a function of π_θ; the derivation is not given. Eq. (15) is asserted as the equivalent policy likelihood, but the equivalence is not proven. More importantly, because Eq. (8) is invalid, the claimed consistency with BT-style preference optimization and the uncertainty-penalty interpretation do not follow. The authors should either provide a careful derivation of Eq. (15) or state explicitly that this is an analogy, not a theorem.
minor comments (6)
  1. [§2.3, Eq. (7)] The sentence "Since the sigmoid function is monotonically increasing" does not by itself imply the inequality; the inequality follows from σ(a)σ(b) ≤ σ(a+b). Please rephrase to state the actual property used.
  2. [Appendix C] The notation is inconsistent: Eq. (6) defines r(x, y_⊥) using a dummy token y_⊥, but Appendix C defines log π(x, y_⊥) as a product over prompt-token hidden states, which is not a single dummy token. Clarify what y_⊥ represents.
  3. [§2.2] Figure 1 is referenced in the discussion of reward comparisons before it is introduced. Reorder or make the cross-reference explicit.
  4. [Introduction] Minor typos: "demonstate" (Section 1) and "Goodhard's law" should be "Goodhart's law."
  5. [§3.1 and §3.2] The term "unpaired data" is used loosely. The reconstructed multiple datasets still contain a designated winning response and three losing responses per prompt; they are not unpaired in the sense of having only one response per prompt. Clarify the intended meaning.
  6. [Appendix D] The dataset construction section says five responses are generated and one is selected as winning; the main text says four responses and three losing. The numbers should be reconciled (main text says "four candidate responses... one winning and three losing," while Appendix D says five).

Circularity Check

2 steps flagged · score 6.0 of 10

Eq. (8) turns a strict lower bound into an equality, and §3.3 presents the anchor's between-class placement—which Eq. (9) is optimized to enforce—as evidence of uncertainty awareness; the unpaired-data derivation inherits the false rewrite.

  1. other [Section 2.3, Eqs. (7)–(8)]
    "Since the sigmoid function is monotonically increasing, we have log σ (r(x, yw) − r(x, y⊥)) + logσ (r(x, y⊥) − r(x, yl)) ≤ log σ (r(x, yw) − r(x, yl)) . (7) Thus, given the definition of utility anchor, we can rewrite the log-likelihood form of Eq. (1) into log p(yw ≻ yl|x) = logσ(r(x, yw) − r(x, y⊥)) + logσ(r(x, y⊥) − r(x, yl)). (8)"

    This is not the usual fit-renamed-as-prediction circularity, but it is an explicit claimed equality that the preceding equation negates. For finite reward differences, σ(a)σ(b)=1/((1+e^{-a})(1+e^{-b})) < 1/(1+e^{-(a+b)})=σ(a+b), so the two-term UAPO objective is strictly less than the BT log-likelihood unless one sigmoid is degenerate. Appendix B later calls Eq. (9) a 'lower bound optimization'. The unpaired-data objective Eq. (11) is justified from the 'separate form of Eq. (8)', so it inherits the unsupported equality rather than being derived from the BT model.

  2. fitted input called prediction [Section 3.3, Figure 1(a)]
    "Figure 1(a) illustrates a visualization that the utility anchor lies between the reward of the winning response and the reward of the losing response, indicating that the anchor effectively classifies responses as either winning or losing."

    The utility anchor is a trainable parameter (Appendix C: a linear function of the prompt's last-layer hidden states). The UAPO loss in Eq. (9) is −E[log σ(r_w−r_⊥)+log σ(r_⊥−r_l)], which is maximized precisely by placing r_⊥ between r_w and r_l (or at least separating them through the sigmoid). Therefore the training curve showing the anchor between the two rewards is the fitted optimum of the loss itself, not empirical evidence that the anchor has discovered an uncertainty threshold. The 'classification performance' presented as an in-depth analysis of the anchor's behavior is enforced by construction and reported as if it were an emergent finding.

full rationale

UAPO's external evaluations on AlpacaEval 2, Arena-Hard, MT-Bench, and RewardBench are standard empirical comparisons against independent baselines; those results are not circular, and no load-bearing self-citation chain is present. The circularity concerns are confined to the claimed derivation chain and the interpretation of the anchor. The paper's central theoretical move (Eq. (7)->Eq. (8)) explicitly states an inequality and then asserts equality, so the advertised 'rewrite' of the BT log-likelihood and the subsequent unpaired-data decomposition are not supported; the manuscript is internally inconsistent because Appendix B describes Eq. (9) as a lower-bound optimization. Separately, the paper presents the anchor's between-class placement during training as evidence that the anchor 'effectively classifies' winning and losing responses, but this placement is exactly the quantity the UAPO objective is optimized to produce. That is a fitted-input-as-empirical-finding pattern. Because the method's practical gains could still be genuine for the surrogate objective, the overall circularity score is partial (6), not total (8-10).

Assumptions & free parameters 3 free parameters · 5 assumptions · 2 invented entities

The central method rests on three fitted quantities (γ, β, and the linear anchor parameters) plus a set of behavioral assumptions about preference modeling and the meaning of the anchor. The most significant unvalidated assumption is that a linear map of prompt hidden states captures annotation uncertainty; this is not tested against alternative anchor parameterizations or genuine unpaired data.

free parameters (3)
  • γ (constant reward anchor) = 1.0, 4.5, 8.0, 16, 20 (set to 0 for multi-dataset experiments)
    Tuned per model and dataset; controls the offset of the utility anchor and dramatically affects training stability (Appendix E.1).
  • β (preference temperature) = 0.01-10.0 depending on method and model
    Selected from a hyperparameter search (Appendix F); scales reward differences and can cause collapse if too large.
  • Utility anchor linear layer parameters W,b = Learned during training (W in R^{1xd}, b scalar)
    Fitted to map prompt hidden states to a scalar reward anchor; the paper provides no independent way to set these, so they are free parameters of the method.
assumptions (5)
  • domain assumption Bradley-Terry model for preferences
    Used in Eq (1) to define the log-likelihood that UAPO claims to rewrite.
  • standard math Sigmoid monotonicity and the inequality σ(a)σ(b) ≤ σ(a+b)
    Invoked in Eq (7) to relate the two sigmoid terms to the original BT term.
  • domain assumption Lower-bound optimization of the BT likelihood yields a useful alignment objective
    The paper never proves that maximizing the lower bound is sufficient for alignment; it is treated as obvious from experiments.
  • ad hoc to paper The learned linear anchor represents annotation uncertainty
    §2.4 interprets the anchor as an uncertainty penalty from pessimistic RL, but no uncertainty estimate is computed; the interpretation is asserted after fitting the anchor.
  • domain assumption Constructed multiple dataset (one winner, three losers per prompt) approximates unpaired feedback
    Appendix D builds these datasets from existing pairwise UltraFeedback data; the paper treats this as evidence for unpaired data training without testing truly unpaired annotations.
invented entities (2)
  • Utility anchor r(x,y⊥)
    purpose: A prompt-dependent scalar threshold that separates winning from losing responses, enabling unpaired training.
    The anchor is a learned linear function of prompt hidden states; its claimed connection to annotation uncertainty is not supported by any measurement outside the paper, and its behavior (lying between winner and loser rewards) is imposed by the loss itself.
  • Dummy token y⊥
    purpose: Symbolic target for the anchor reward in the loss equations.
    Pure notation; not an observable or physically meaningful entity. It is introduced to define the anchor and has no independent falsifiable handle.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Adaptive Preference Optimization with Uncertainty-aware Utility Anchor." pith.science (2026). https://pith.science/paper/YZFSBQNM

@misc{pith2026250910515,
  author       = {Pith},
  title        = {Pith review of: Adaptive Preference Optimization with Uncertainty-aware Utility Anchor},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YZFSBQNM}},
  note         = {Machine review of arXiv:2509.10515}
}
read the original abstract

Offline preference optimization methods are efficient for large language models (LLMs) alignment. Direct Preference optimization (DPO)-like learning, one of the most popular approaches, stands out for its efficiency in reward modeling. However, these methods typically follow the convention to use Bradley-Terry (BT) reward modeling that faces several critical assumptions, including the requirement for pairwise training data, model distribution shifting, human rationality assumption, etc. To address these limitations, we propose a general framework for offline preference optimization methods, Adaptive Preference Optimization with Utility Anchor (UAPO), which introduces an anchoring function to estimate the uncertainties brought from preference data annotation. Our method enables training even in scenarios where the data is unpaired, significantly enhancing data utilization efficiency. Moreover, the anchor design makes UAPO more robust in the training process. Experimental results demonstrate that UAPO achieves competitive outcomes without the strict dependency on data pairing, paving the way for more flexible and effective preference optimization methods.

Figures

Figures reproduced from arXiv: 2509.10515 by the authors.

Figure 1
Figure 1. Analysis of utility anchor representation during SimUAPO training on Gemma-2-Instruct: (a) Relationship [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Analysis of KL divergence on Mistral-Instruct. [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

29 extracted references · 25 canonical work pages

  1. [1]

    Begin with 5-10 minutes a day

    Start Small and Be Realistic: Tiny Habits: Don’t aim for masterpieces right away. Begin with 5-10 minutes a day. You’d be surprised how much you can accomplish even in short bursts. Easy Wins: Choose simple subjects to start with: doodles, shapes, still lifes, or even tracing. Build confidence with achievable goals

    work page

  2. [2]

    In 9th International Conference on Learn- ing Representations, ICLR 2021, Virtual Event, Aus- tria, May 3-7, 2021

    Measuring massive multitask language under- standing. In 9th International Conference on Learn- ing Representations, ICLR 2021, Virtual Event, Aus- tria, May 3-7, 2021. Jiwoo Hong, Noah Lee, and James Thorne. 2024. Orpo: Monolithic preference optimization without refer- ence model. In Proceedings of the 2024 Conference on Empirical Methods in Natural Lang...

    arXiv 2021

  3. [3]

    Gather Supplies: Have your pencils, paper, erasers, etc., readily available

    Create a Conducive Environment:Designated Space: Set up a comfortable and inspiring drawing area. Gather Supplies: Have your pencils, paper, erasers, etc., readily available

    work page

  4. [4]

    Quiet Space: Find a spot where you won’t be interrupted

    Remove Distractions: Phone on Silent: Put your phone away during your drawing time. Quiet Space: Find a spot where you won’t be interrupted

    work page

  5. [5]

    Focus on the process, not the outcome

    Embrace Imperfection: Don’t Edit: Let go of the need for perfection. Focus on the process, not the outcome. Experiment: Try different styles, tools, and subjects. Don’t be afraid to make mistakes—they’re part of the learning process

    work page

  6. [6]

    Put it in your calendar and stick to it as much as possible

    Schedule It In: Treat it like an appointment: Choose a specific time each day dedicated to drawing. Put it in your calendar and stick to it as much as possible. Morning Ritual: Drawing first thing in the morning can set a creative tone for the day. Evening Wind-Down: If you’re more relaxed in the evenings, drawing can be a calming way to de-stress

    work page

  7. [7]

    Join a Community: Online Groups: Connect with other artists for support, feedback, and inspiration.Local Classes: Consider taking a drawing class to learn new techniques and meet fellow artists

    work page

  8. [8]

    Seeing your improvement over time can be incredibly motivating.Photo Journal: Take photos of your finished drawings to document your journey

    Track Your Progress: Sketchbook: Keep a sketchbook to record your daily drawings. Seeing your improvement over time can be incredibly motivating.Photo Journal: Take photos of your finished drawings to document your journey

    work page

Show all 29 references
  1. [9]

    Enjoy the Process: The most important reward is the joy of creating! Remember, consistency is key

    Reward Yourself: Celebrate Milestones: Treat yourself when you reach a drawing goal. Enjoy the Process: The most important reward is the joy of creating! Remember, consistency is key. Even if you only draw for a few minutes each day, you’ll see progress over time. Be patient, ...

  2. [10]

    Find Inspiration: Follow Artists: Look at the work of artists you admire.Take Photos: Capture interesting scenes or objects to draw later.Visit Museums: Observe art in person

  3. [14]

    Begin with 15-20 minutes of drawing

    Set Realistic Goals: Start Small: Don’t pressure yourself to create master- pieces every day. Begin with 15-20 minutes of drawing. Focus on Consistency: Aim for daily practice, even if it’s just quick sketches. It’s about building the habit, not producing perfect artwork

  4. [15]

    Experiment with Mediums: Don’t limit yourself to pen- cils

    Make it Enjoyable: Choose Subjects You Love: Draw things that genuinely interest you – people, animals, landscapes, objects, patterns – anything that sparks your creativity. Experiment with Mediums: Don’t limit yourself to pen- cils. Explore pens, markers, watercolors, digital...

  5. [16]

    Create a Routine: Schedule It In: Treat your drawing time like an impor- tant appointment. Block it out on your calendar and stick to it as much as possible.Find Your Ideal Time: Experiment with different times of day to see when you’re most creative.Set Up a Dedicated Space: ...

  6. [17]

    Embrace them as opportunities to grow

    Embrace the Process: Don’t Be Afraid to Make Mistakes: Mistakes are part of learning. Embrace them as opportunities to grow. Focus on Progress, Not Perfection: Celebrate small victories and track your improvement over time.Learn from Others: Study the work of artists you admir...

  7. [18]

    Feedback and encour- agement can be incredibly motivating

    Stay Motivated: Join a Challenge: There are many online drawing chal- lenges that can provide structure and accountability.Share Your Work: Show your drawings to friends, family, or online communities. Feedback and encour- agement can be incredibly motivating. Reward Yourself:...

  8. [19]

    draw for an hour,

    Start Small & Be Realistic: Don’t aim for masterpieces: Begin with 5-10 minutes a day. Even doodling is progress! Set achievable goals: Instead of "draw for an hour," try "sketch something for 15 minutes." Be kind to yourself: Some days will be better than others. Don’t get di...

  9. [20]

    Experiment with different mediums: Explore pencils, pens, charcoal, watercolors, digital tools – find what feels right

    Make it Enjoyable: Choose subjects you love: Draw things that excite you, whether it’s animals, landscapes, people, or abstract shapes. Experiment with different mediums: Explore pencils, pens, charcoal, watercolors, digital tools – find what feels right. Listen to music or po...

  10. [21]

    Set reminders: Use your phone, calendar, or sticky notes to keep yourself on track

    Build a Routine: Pick a specific time: Morning, lunch break, evening – find a time that works best for you and stick to it. Set reminders: Use your phone, calendar, or sticky notes to keep yourself on track. Create a dedicated space: Having a designated drawing area can signal...

  11. [22]

    Follow artists you admire: Look at their work, learn from their techniques, and get inspired

    Stay Inspired: Carry a sketchbook: Jot down ideas, quick sketches, or observations throughout the day. Follow artists you admire: Look at their work, learn from their techniques, and get inspired. Take online classes or workshops: Learn new skills and challenge yourself

  12. [23]

    Don’t be afraid to make mistakes: They’re opportunities to learn and grow

    Embrace the Process: Focus on the journey, not just the destination: Enjoy the act of creating, even if the final result isn’t perfect. Don’t be afraid to make mistakes: They’re opportunities to learn and grow. Celebrate your progress: Track your drawings, look back on how far...

  13. [24]

    draw for an hour,

    Start Small and Be Realistic: Don’t aim for masterpieces: Begin with 5- 10 minutes a day. Focus on consistency over quantity or quality initially. Set achievable goals: Instead of "draw for an hour," try "draw for 15 minutes before breakfast."

  14. [25]

    Experiment with different mediums: Don’t limit yourself to pencils

    Make it Enjoyable: Choose subjects you love: Draw things that spark your interest, whether it’s nature, people, animals, or abstract shapes. Experiment with different mediums: Don’t limit yourself to pencils. Try pens, charcoal, watercolors, digital tools – explore and find wh...

  15. [26]

    Block out a specific time in your day and stick to it

    Create a Routine: Schedule it in: Treat drawing like any other important appointment. Block out a specific time in your day and stick to it. Set up your space: Have your drawing materials easily accessible in a dedicated area. Use a timer: Knowing you only have a set amount of...

  16. [27]

    Embrace them and learn from them

    Embrace Imperfection: Don’t be afraid to make mistakes: Mistakes are part of the learning process. Embrace them and learn from them. Don’t compare yourself to others: Everyone progresses at their own pace. Focus on your own journey and celebrate your progress

  17. [28]

    Visit museums or galleries: Expose yourself to different art forms and perspectives

    Find Inspiration: Follow artists you admire: Observe their techniques and styles. Visit museums or galleries: Expose yourself to different art forms and perspectives. Draw from life: Observe the world around you and capture what you see

  18. [29]

    Share your work: Show your drawings to friends, family, or online communities for feedback and encouragement

    Track Your Progress and Celebrate Milestones:Keep a sketchbook: Doc- ument your daily drawings and track your progress over time. Share your work: Show your drawings to friends, family, or online communities for feedback and encouragement. Reward yourself: Acknowledge your ach...

    2024

  19. [2021]

    CoRR, abs/2110.14168

    Training verifiers to solve math word prob- lems. CoRR, abs/2110.14168. Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun. 2023. Ultrafeedback: Boosting lan- guage models with high-quality feedback. Preprint, arXiv:2310...

    2023 arXiv

  20. [2022]

    arXiv preprint arXiv:2203.02155

    Training language models to follow in- structions with human feedback. arXiv preprint arXiv:2203.02155. Ryan Park, Rafael Rafailov, Stefano Ermon, and Chelsea Finn. 2024. Disentangling length from qual- ity in direct preference optimization. In Findings of the Association for ...

    2024 arXiv

  21. [2024]

    arXiv preprint arXiv:2405.14734

    Simpo: Simple preference optimization with a reference-free reward. arXiv preprint arXiv:2405.14734. Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Car- roll L Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al

    arXiv

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.