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Training on failures teaches small LLMs to backtrack and recover

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2026-07-09 09:08 UTC pith:S666ZHDR

load-bearing objection Pyligent teaches small LLMs to backtrack using validator-labeled failed branches. The hidden graph task is a clean probe and the gains are real, but the paper only compares against gold-only SFT — a baseline that structurally cannot backtrack — so the specific contribution of Pyligent's design choices over simpler search-trace training is unisolated. the 3 major comments →

arxiv 2607.07492 v1 pith:S666ZHDR submitted 2026-07-08 cs.AI

Search, Fail, Recover: A Training Framework for Correction-Aware Reasoning

Dmitry Beresnev , Vladimir Makharev , Roman Khalikov , Ivan Oseledets , Petr Anokhin This is my paper
classification cs.AI
keywords backtrackingfailed-branch supervisionchain-of-thoughtsearch-based reasoningvalidator-guided explorationrecovery behaviorsmall language modelssupervised fine-tuning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper introduces Pyligent, a training framework that teaches language models to search, detect when a reasoning branch has failed, and backtrack to the right earlier choice point. The core claim is that explicit supervision on validator-labeled failed branches — not just polished successful solution chains — is what enables useful recovery behavior. The framework represents reasoning as a tree of partial solution chains, uses a task-specific validator to label continuations as valid or invalid, and converts both successful and failed branches into supervised training examples for three actions: continue, finish, and backtrack. On a hidden directed graph task designed to isolate delayed-failure recovery, a 0.6B-parameter model trained with Pyligent achieves 76.5% success versus 3.8% for gold-only supervised fine-tuning, with 47.7% of its backtracks classified as 'perfect' — returning to the exact choice point responsible for the failure. Similar gains appear on 4×4 Sudoku (17–18 point improvements) and Blocksworld (13 points). The paper argues that reliable backtracking does not emerge from imitating correct solutions alone; it must be represented in the action space, generated during exploration, validated in hindsight, and trained as an explicit target.

Core claim

The central object is the ChainTree: a data structure that stores both accepted and rejected branches of a reasoning search, assigns backtrack targets for failed branches, and converts the resulting search tree into prefix-to-action training examples. The key empirical finding is that supervision derived from failed branches — where a validator identifies that a locally plausible continuation leads to a dead end, and the correct recovery point is the latest prefix that can still be completed — produces recovery behavior that gold-only training cannot. On the hidden directed graph task, gold-only SFT learns the surface format of successful paths but rarely recovers from mistakes (3.8% success

What carries the argument

Pyligent's three-stage pipeline: (1) SFT-A trains on gold solution chains, (2) validator-guided exploration samples continuations from selected prefixes and records both valid and failed branches, (3) SFT-B fine-tunes on the resulting ChainTree examples including success pairs, failure pairs (teaching <backtrack> actions), and continue pairs (teaching recovery after a runtime-injected trace summarizing the abandoned branch). A reverse curriculum starts exploration near the solution and progressively moves toward the root.

Load-bearing premise

The claim that Pyligent teaches generalizable recovery behavior rests on the assumption that the hidden directed graph task, 4×4 Sudoku, and Blocksworld — all of which have exact, instantaneous validators — are representative enough to predict behavior on broader reasoning domains where validation is weaker, delayed, or ambiguous.

What would settle it

If a model trained with Pyligent's failed-branch supervision on the hidden graph task shows no transfer of backtracking behavior to a new task with a different action format and validator structure, then the learned recovery is task-specific formatting rather than a generalizable search-and-recover policy.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • If failed-branch supervision is the missing ingredient for teaching recovery, then standard chain-of-thought datasets that contain only polished solutions are systematically incomplete for training models that can self-correct.
  • The separation between learning to emit a syntactically valid backtrack and learning the full policy of when to abandon and where to resume suggests that current training recipes may produce shallow backtracking rather than genuine search behavior.
  • The framework's reliance on exact, instantaneous validators means its strongest results (hidden graphs) apply to a regime that most real-world reasoning tasks do not satisfy; extending to weaker or delayed validation is the open frontier.
  • The traced recovery mechanism — injecting a compact summary of the abandoned branch after backtracking — hints that models may need structured memory of failures to avoid repeating mistakes, rather than simply discarding failed context.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the key signal is the structure of failed attempts and the correct point of return, then data-efficient training might benefit from actively generating harder failures (branches that fail far from their origin) rather than uniformly sampling exploration continuations.
  • The gap between 'valid' backtracks and 'perfect' backtracks suggests a potential two-stage training decomposition: first teach failure detection and backtrack syntax, then separately optimize backtrack target selection through targeted reward or curriculum.
  • The reverse curriculum — starting near solutions and moving toward roots — implicitly assumes that recovery is easier to learn when the remaining search is short; this could fail on tasks where the difficulty of detecting a failure is independent of remaining depth.
  • The framework could be tested on tasks with probabilistic validators (where validation is correct with some error rate) to determine whether the recovery behavior degrades gracefully or collapses when the validator itself is noisy.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 7 minor

Summary. The manuscript introduces Pyligent, a training framework that operationalizes the Diligent Learner formulation [Shalev-Shwartz and Shashua, 2025] for small LLMs. The framework represents reasoning as validated search over partial solution chains: a task validator labels generated continuations and failures, and the resulting search trees are converted into supervised targets for continue, finish, and backtrack actions, with optional traces summarizing abandoned branches. Pyligent is evaluated on a hidden directed graph task designed to isolate delayed-failure recovery, 4x4 Sudoku (with and without reasoning traces), and Blocksworld. Compared to gold-only SFT, Pyligent shows substantial improvements, most notably 76.5% vs 3.8% success on the hidden graph task, with 47.7% of backtracks classified as 'perfect.'

Significance. The paper makes a solid empirical contribution by translating the theoretical conditions of the Diligent Learner into a concrete, implementable training pipeline. The hidden directed graph task is a well-designed probe that cleanly isolates the recovery mechanism. The backtrack quality taxonomy (invalid/valid/correct/perfect) is a useful diagnostic tool. Ablations over traces and failed-path epochs provide informative negative results (e.g., more failed-path epochs can improve backtrack quality without improving final solve rate). The framework is not circular: the validator is task-specific and external to the learned policy, and backtrack targets are derived from search tree structure rather than from the model's own outputs. The primary limitation, acknowledged by the authors, is that all tasks require exact, instantaneous validators.

major comments (3)
  1. §5, Table 2: The primary comparison is Pyligent vs gold-only SFT. Gold-only SFT structurally cannot produce backtracking behavior, so this comparison establishes only that some search-structured training helps, not that Pyligent's specific mechanism (validator-labeled failed branches, traced recovery, reverse curriculum, dendritic explorer) is the effective ingredient. The paper cites Stream of Search [Gandhi et al., 2024], Self-Backtracking [Yang et al., 2025], and ASTRO [Kim et al., 2025] as related methods that also train on search traces containing failures and recoveries (§2), but does not compare against any of them. Without at least one search-training baseline on the same base model and data budget, the specific contribution of validator-labeled failed-branch supervision is unisolated. This is load-bearing for the central claim that 'explicit failed-branch supervision teaches有用re
  2. §5.2, Table 2: The 'model FT' vs 'Pyligent' comparison is intended to show that the iterative training-and-exploration loop is useful beyond just collecting a larger supervised dataset. However, the differences are inconsistent: on Sudoku 4x4 (expert), Linear (66%) outperforms Linear FT (56%), but Dendritic (54%) underperforms Dendritic FT (55%). On the mixed dataset, Linear (82%) outperforms Linear FT (69%), but Dendritic (81%) only slightly outperforms Dendritic FT (74%). The paper does not explain why the iterative loop helps substantially in some configurations but not others, or why the dendritic explorer underperforms the linear explorer on expert Sudoku by 12 points. This makes it difficult to assess which components of the pipeline are load-bearing.
  3. §5.3, Table 3: On Blocksworld, only 14.4% of Pyligent's backtracks are classified as 'perfect,' yet the model still achieves a 21-point improvement over gold-only SFT. This is a surprisingly low perfect-backtrack rate for the framework's most complex task, and the paper does not adequately explain how substantial gains are achieved despite imperfect recovery targeting. The §6 conclusion acknowledges that 'learning to emit a syntactically valid recovery action is easier than learning the full policy of when to abandon a branch and where to resume,' but this does not resolve the tension between the low perfect-backtrack rate and the large solve-rate gain. A breakdown of how many successes involved at least one valid or correct (non-perfect) backtrack would help clarify the mechanism.
minor comments (7)
  1. §5.1: '427.7±31.1 backtracks across 200 examples' — clarify whether this is total backtracks or per-example average. The number seems to be total, but the phrasing is ambiguous given that 'Avg. backtracks' in Table 1 is reported as 2.14±0.16.
  2. §4.4: 'we preserved tasks with up to 16 actions required to solve them' for training, but the test set uses 'full plan-length distribution with up to 42 actions.' The training/test distribution mismatch is noted but its implications are not discussed. Figure 12 shows performance declining with action count; a brief comment on extrapolation beyond the training range would be useful.
  3. §5.2.2: 'While reasoning traces appear to be more harmful than helpful in this particular setting' — this claim is not supported by an ablation comparing Sudoku with and without reasoning traces on the same data split. The w/ RT results use different training data (reasoning generated by Qwen3-32B) and are compared against standard Sudoku, making it difficult to isolate the effect of traces.
  4. Table 2: The 'Sudoku w/ RT' rows only report Pyligent and Gold-only SFT, with no model FT or dendritic variants. Briefly note why these were omitted.
  5. §8.5: The hidden graph task uses 300 training examples and Qwen3-0.6B, while Sudoku and Blocksworld use Qwen3-4B. The rationale for different model sizes across tasks is not explained.
  6. Figure 1: The diagram is dense and some text labels (e.g., 'DEFAULT / CUMULATIVE,' 'NON-TRACED / TRACED') are difficult to parse. Consider simplifying or adding a legend.
  7. §3.2: The reverse curriculum is described as starting 'near the end of a gold chain' and moving 'toward the root,' but Appendix 8.2 describes it as starting with T=t and decreasing to T=1. Clarify the relationship between the curriculum parameter T and the chain length in the main text.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for a careful and constructive report. The referee correctly identifies that our primary comparison (Pyligent vs. gold-only SFT) establishes that search-structured training helps, but does not isolate which specific components of Pyligent are responsible. We agree this is a significant gap and will add at least one search-training baseline. We also agree that the inconsistent model-FT vs. Pyligent results and the low perfect-backtrack rate on Blocksworld require more analysis. We address each comment below.

read point-by-point responses

  1. Referee: §5, Table 2: The primary comparison is Pyligent vs gold-only SFT. Gold-only SFT structurally cannot produce backtracking behavior, so this comparison establishes only that some search-structured training helps, not that Pyligent's specific mechanism is the effective ingredient. Without at least one search-training baseline on the same base model and data budget, the specific contribution of validator-labeled failed-branch supervision is unisolated.

    Authors: The referee is correct that our current comparison does not isolate which specific ingredient of Pyligent drives the gains. We agree that a search-training baseline is needed. In the revision, we will add at least one comparison against Stream of Search [Gandhi et al., 2024] on the same base model (Qwen3-4B) and data budget for the Sudoku and Blocksworld tasks. We chose Stream of Search as the most directly comparable baseline because it also serializes search traces containing failures and recoveries, but differs from Pyligent in how failed branches are labeled and converted to supervision. If implementation constraints prevent a full comparison on all tasks within the revision timeline, we will at minimum provide it on one task (Sudoku 4x4 mixed) and discuss the comparison's scope and limitations explicitly. We will also temper the central claim in the abstract and conclusion to state that 'explicit failed-branch supervision can teach useful recovery behavior' rather than implying it is the uniquely effective ingredient. revision: yes

  2. Referee: §5.2, Table 2: The 'model FT' vs 'Pyligent' comparison shows inconsistent differences. On expert Sudoku, Linear (66%) outperforms Linear FT (56%), but Dendritic (54%) underperforms Dendritic FT (55%). On the mixed dataset, Linear (82%) outperforms Linear FT (69%), but Dendritic (81%) only slightly outperforms Dendritic FT (74%). The paper does not explain why the iterative loop helps substantially in some configurations but not others, or why the dendritic explorer underperforms the linear explorer on expert Sudoku by 12 points.

    Authors: The referee is right that we did not adequately explain these inconsistencies. We do not currently have a definitive explanation for why the iterative loop helps more in some configurations than others, or why the dendritic explorer underperforms on expert Sudoku. Our hypothesis is that the dendritic explorer's broad-then-deep schedule may be less effective when the puzzle distribution is homogeneous (expert-only), because breadth at shallow depth is less informative when all instances have similar difficulty and structure. However, we have not verified this. In the revision, we will (1) add per-configuration backtrack-quality breakdowns to test whether the differences correlate with backtrack quality or with continuation quality, and (2) explicitly acknowledge that we cannot fully explain these inconsistencies and flag the explorer choice as an open empirical question. We will not claim that the iterative loop is uniformly beneficial. revision: partial

  3. Referee: §5.3, Table 3: On Blocksworld, only 14.4% of Pyligent's backtracks are classified as 'perfect,' yet the model still achieves a 21-point improvement over gold-only SFT. The paper does not adequately explain how substantial gains are achieved despite imperfect recovery targeting. A breakdown of how many successes involved at least one valid or correct (non-perfect) backtrack would help clarify the mechanism.

    Authors: The referee correctly identifies a tension we did not resolve. We will add the requested breakdown: how many successful Blocksworld solutions involved at least one valid, correct, or perfect backtrack. We already report in Appendix 8.7 (Figure 12) that 'the number above each bar indicates how many of the successful tasks involved at least one backtrack,' but we did not break this down by backtrack quality category. We will add this breakdown in the revision. Our current hypothesis is that on Blocksworld, even non-perfect backtracks (e.g., valid-only) can be useful because the model may emit multiple backtracks in sequence, eventually reaching a productive recovery point. The 21-point gain may also partly come from improved forward planning learned through exploration, not solely from backtracking. We will state this hypothesis explicitly and note that the relative contribution of backtracking vs. improved forward planning is not fully disentangled by our current experiments. revision: yes

Circularity Check

0 steps flagged

No circularity found: the framework's training targets are derived from external validators and search-tree structure, not from the model's own outputs fed back as definitions.

full rationale

The paper's central claim is that explicit failed-branch supervision teaches useful recovery behavior beyond gold-only SFT. Walking the derivation chain: (1) The validator is task-specific and external (graph visibility rules, Sudoku constraint checks, VAL plan simulator), not defined in terms of the model's learned policy. (2) Backtrack targets are constructed from search-tree structure: for a failed branch diverging after node i reaching invalid leaf j, the target is c_j → <backtrack>i r</backtrack>, where r is a validator-derived reason (§3.2). This is a structural mapping from tree geometry, not a quantity defined in terms of the model's output. (3) The Diligent Learner theory [Shalev-Shwartz and Shashua, 2025] is cited as conceptual motivation, but the paper explicitly frames itself as an 'empirical implementation study' testing whether the theory's behavioral conditions can be operationalized (§6). The theory is not used to forbid alternatives or declare the approach uniquely forced. (4) The paper cites Stream of Search, Self-Backtracking, and ASTRO as related methods but does not claim to derive its approach from them via a self-citation chain. (5) No fitted parameter is renamed as a prediction: the improvements over gold-only SFT are measured against held-out evaluation sets, and the ablations (traces vs. no-traces, explorer type, failed-path epochs) test components independently. The absence of search-training baselines (SoS, Self-Backtracking, ASTRO) is a correctness/completeness concern, not a circularity issue. The derivation is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

9 free parameters · 4 axioms · 3 invented entities

The framework introduces one new benchmark (hidden graph), one evaluation taxonomy (backtrack categories), and one inference mechanism (traced recovery). All three have independent falsifiable handles. The free parameters are mostly standard training hyperparameters; the dendritic explorer parameters are unstated, which is a reproducibility gap. The axioms are inherited from the Diligent Learner theory and are reasonable for the tested domains but untested beyond them.

free parameters (9)
  • Branching budget B = 5
    Controls number of parallel chains in linear explorer; set by hand (Appendix 8.1).
  • Leaf multiplier c_leaf = 3
    Controls leaf expansion in exploration; set by hand (Appendix 8.1).
  • Maximum leaf capability = 20
    Caps total leaves; set by hand (Appendix 8.1).
  • LoRA rank = 32
    Adapter rank for QLoRA fine-tuning; standard choice but not derived.
  • LoRA alpha = 64
    Adapter scaling; set to 2× rank by convention.
  • Learning rate = 5e-5
    SFT learning rate; standard range, not tuned systematically in the paper.
  • Dendritic explorer parameters (s, h, α_bfs, α_dfs, β) = not stated
    Control BFS-to-DFS transition in dendritic explorer; values not given in the paper (Appendix 8.1).
  • Failed-path epoch count = 1 or 3
    Controls weight of failed-branch supervision; ablated but optimal value is task-dependent and not derived.
  • Evaluation step budget = 30 (Sudoku, hidden graph), 50 (Blocksworld)
    Maximum inference steps before timeout; set by hand.
axioms (4)
  • domain assumption Validation is easier than generation for the target task domains.
    Borrowed from Diligent Learner theory (§1). The paper instantiates this with exact validators for each task, but the assumption is what makes the framework applicable — it does not hold for all reasoning domains.
  • domain assumption The model can generate a correct next reasoning step with fixed nonzero probability after SFT-A.
    Diligent Learner sufficient condition (§1). The reverse curriculum and exploration assume the model has some chance of producing valid continuations from prefixes; if this probability is effectively zero, exploration produces no useful data.
  • domain assumption Backtracking to the maximal repairable prefix is the correct recovery target.
    From Diligent Learner theory (§2). The ChainTree assigns backtrack targets based on this principle. The 'perfect' backtrack category operationalizes it, but the paper does not test whether alternative recovery strategies (e.g., restarting, partial rollback) would perform comparably.
  • ad hoc to paper Small synthetic tasks with exact validators are informative proxies for broader reasoning.
    Implicit in the experimental design. The paper acknowledges this limitation in §7 but the central claims are staked on these tasks.
invented entities (3)
  • Hidden directed graph task independent evidence
    purpose: Controlled benchmark isolating delayed-failure recovery under partial observability.
    The task is a new benchmark with a clear formal definition (§4.1). It provides falsifiable predictions: a model without backtracking cannot solve it. The task structure is simple enough to verify claims about recovery behavior.
  • Backtrack quality taxonomy (invalid/valid/correct/perfect) independent evidence
    purpose: Evaluation framework for classifying backtrack actions beyond binary success/failure.
    The taxonomy (Table 4) is a measurement tool, not a theoretical entity. It is falsifiable: different models can be scored and compared. The nesting property (perfect ⊂ correct ⊂ valid ⊂ non-invalid) is well-defined.
  • Traced recovery (<trace> injection) independent evidence
    purpose: Preserve compact failure information after context truncation to prevent repeating mistakes.
    The trace mechanism is an engineering contribution with a falsifiable claim: traced recovery should outperform reset/preserve modes. The Sudoku ablation (Figure 4) tests this and finds traces help.

pith-pipeline@v1.1.0-glm · 20003 in / 3514 out tokens · 439651 ms · 2026-07-09T09:08:39.894183+00:00 · methodology

0 comments
read the original abstract

Many reasoning tasks are not well described by a single left-to-right chain: a solver may need to pursue a plausible branch, observe delayed failure, and return to the latest prefix that can still be completed. We introduce Pyligent, a training and inference framework inspired by the Diligent Learner formulation that represents reasoning as validated search over partial solution chains. A task validator labels generated continuations and failures, and the resulting search trees are converted into supervised targets for three actions: continue, finish, and backtrack, with optional traces that summarize abandoned branches. We evaluate Pyligent on a hidden directed graph task designed to isolate delayed-failure recovery, and on structured reasoning domains with exact validators, including $4{\times}4$ Sudoku, Sudoku with reasoning traces, and Blocksworld. Compared with gold-only supervised fine-tuning, Pyligent improves solve rate by $72.7$ percentage points on hidden graphs, by $17$ and $18$ points on mixed and expert Sudoku, by $27$ and $14$ points on mixed and expert Sudoku with reasoning traces, and by $13$ points on Blocksworld. These results suggest that explicit failed-branch supervision can teach useful recovery behavior beyond imitation of polished solution chains.

Figures

Figures reproduced from arXiv: 2607.07492 by Dmitry Beresnev, Ivan Oseledets, Petr Anokhin, Roman Khalikov, Vladimir Makharev.

Figure 1
Figure 1. Figure 1: The Pyligent framework. The pipeline iterates for [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Hidden directed graph interaction. The environment contains a full graph, but the prompt [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Hidden directed graph backtrack quality. Pie charts show mean backtrack counts over [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Sudoku 4×4 ablations on the mixed dataset with Qwen3-4B, reported as mean ± standard deviation over three runs. ‘No traces’ uses architectures without traces and one failed-path epoch. ‘Traces’ uses architectures with traces and one failed-path epoch, matching the first-row setting in [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Sudoku 4×4 backtrack quality on the mixed dataset with Qwen3-4B. Pie charts show mean backtrack counts over three runs. All listed architectures use traces. 82 ± 1.0%, compared with 65 ± 0.3% for gold-only SFT and 11 ± 0.9% for the base model ( [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Blocksworld backtrack quality. Pie charts show mean backtrack counts over three runs, [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Example of Sudoku 4×4 trees. Type Description Example Invalid Target is missing, points to the current last node, or is emitted after <done>. <node>2 wrong</node> <backtrack>2</backtrack> Valid Target exists and is earlier than the current node, but it is not confirmed as the best correction point. <node>1 correct</node> <node>2 correct</node> <backtrack>1</backtrack> Correct Chosen target is a correct sta… view at source ↗
Figure 8
Figure 8. Figure 8: Exploration schedules. Linear exploration spawns a bounded number of parallel chains, [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Example of different SFT-B pair kinds for prefix [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Finetune expander examples for prefix <node>0 puzzle input</node>, <node>1 Step A</node>. Format is [training prefix] –> training target. utilization 0.8, gradient checkpointing and 10 dataloader workers and exploration batch size 256. For exploration we use a linear explorer with branching budget B = 5, leaf multiplier cleaf = 3, maximum leaf capability 20, adaptive Tmax (so the maximum curriculum T is t… view at source ↗
Figure 11
Figure 11. Figure 11: Distribution of task outcomes on the Blocksworld test set for the Gold-only SFT model [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Distribution of Blocksworld successes and backtracks across the number of actions in the [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗

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Reference graph

Works this paper leans on

31 extracted references · 31 canonical work pages · 24 internal anchors

  1. [1]

    When is Tree Search Useful for

    Chen, Ziru and White, Michael and Mooney, Ray and Payani, Ali and Su, Yu and Sun, Huan , editor =. When is Tree Search Useful for. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , publisher =. 2024 , file =. doi:10.18653/v1/2024.acl-long.738 , shorttitle =

    work page doi:10.18653/v1/2024.acl-long.738 2024

  2. [2]

    CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

    Gou, Zhibin and Shao, Zhihong and Gong, Yeyun and Shen, Yelong and Yang, Yujiu and Duan, Nan and Chen, Weizhu , urldate =. 2024 , langid =. doi:10.48550/arXiv.2305.11738 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2305.11738 2024

  3. [3]

    Gandhi, Kanishk and Lee, Denise H. J. and Grand, Gabriel and Liu, Muxin and Cheng, Winson and Sharma, Archit and Goodman, Noah , urldate =. Stream of Search (. 2024 , langid =. doi:10.48550/arXiv.2404.03683 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2404.03683 2024

  4. [4]

    DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

    2025 , eprinttype =. doi:10.1038/s41586-025-09422-z , shorttitle =. 2501.12948 [cs] , keywords =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1038/s41586-025-09422-z 2025

  5. [5]

    Rea- soning with Language Model is Planning with World Model

    Hao, Shibo and Gu, Yi and Ma, Haodi and Hong, Joshua and Wang, Zhen and Wang, Daisy and Hu, Zhiting , editor =. Reasoning with Language Model is Planning with World Model , url =. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , publisher =. 2023 , file =. doi:10.18653/v1/2023.emnlp-main.507 , abstract =

    work page doi:10.18653/v1/2023.emnlp-main.507 2023

  6. [6]

    Large Language Models Cannot Self-Correct Reasoning Yet

    Huang, Jie and Chen, Xinyun and Mishra, Swaroop and Zheng, Huaixiu Steven and Yu, Adams Wei and Song, Xinying and Zhou, Denny , urldate =. Large Language Models Cannot Self-Correct Reasoning Yet , url =. 2024 , langid =. doi:10.48550/arXiv.2310.01798 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2310.01798 2024

  7. [7]

    When Can

    Kamoi, Ryo and Zhang, Yusen and Zhang, Nan and Han, Jiawei and Zhang, Rui , urldate =. When Can. 2024 , file =. doi:10.1162/tacl_a_00713 , shorttitle =

    work page doi:10.1162/tacl_a_00713 2024

  8. [8]

    Team, Kimi and Du, Angang and Gao, Bofei and Xing, Bowei and Jiang, Changjiu and Chen, Cheng and Li, Cheng and Xiao, Chenjun and Du, Chenzhuang and Liao, Chonghua and Tang, Chuning and Wang, Congcong and Zhang, Dehao and Yuan, Enming and Lu, Enzhe and Tang, Fengxiang and Sung, Flood and Wei, Guangda and Lai, Guokun and Guo, Haiqing and Zhu, Han and Ding, ...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2501.12599 2025

  9. [9]

    Training Language Models to Self-Correct via Reinforcement Learning

    Kumar, Aviral and Zhuang, Vincent and Agarwal, Rishabh and Su, Yi and Co-Reyes, John D. and Singh, Avi and Baumli, Kate and Iqbal, Shariq and Bishop, Colton and Roelofs, Rebecca and Zhang, Lei M. and. Training Language Models to Self-Correct via Reinforcement Learning , url =. 2024 , langid =. doi:10.48550/arXiv.2409.12917 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2409.12917 2024

  10. [10]

    Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping

    Lehnert, Lucas and Sukhbaatar, Sainbayar and Su,. Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping , url =. 2024 , langid =. doi:10.48550/arXiv.2402.14083 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2402.14083 2024

  11. [11]

    Self-Refine: Iterative Refinement with Self-Feedback

    Madaan, Aman and Tandon, Niket and Gupta, Prakhar and Hallinan, Skyler and Gao, Luyu and Wiegreffe, Sarah and Alon, Uri and Dziri, Nouha and Prabhumoye, Shrimai and Yang, Yiming and Gupta, Shashank and Majumder, Bodhisattwa Prasad and Hermann, Katherine and Welleck, Sean and Yazdanbakhsh, Amir and Clark, Peter , urldate =. Self-Refine: Iterative Refinemen...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2303.17651 2023

  12. [12]

    Recursive Introspection: Teaching Language Model Agents How to Self-Improve

    Qu, Yuxiao and Zhang, Tianjun and Garg, Naman and Kumar, Aviral , urldate =. Recursive Introspection: Teaching Language Model Agents How to Self-Improve , url =. 2024 , langid =. doi:10.48550/arXiv.2407.18219 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2407.18219 2024

  13. [13]

    To Backtrack or Not to Backtrack: When Sequential Search Limits Model Reasoning , url =

    Qin, Tian and Alvarez-Melis, David and Jelassi, Samy and Malach, Eran , urldate =. To Backtrack or Not to Backtrack: When Sequential Search Limits Model Reasoning , url =. 2025 , langid =. doi:10.48550/arXiv.2504.07052 , eprint =

    work page doi:10.48550/arxiv.2504.07052 2025

  14. [14]

    Spurious Rewards: Rethinking Training Signals in RLVR

    Shao, Rulin and Li, Shuyue Stella and Xin, Rui and Geng, Scott and Wang, Yiping and Oh, Sewoong and Du, Simon Shaolei and Lambert, Nathan and Min, Sewon and Krishna, Ranjay and Tsvetkov, Yulia and Hajishirzi, Hannaneh and Koh, Pang Wei and Zettlemoyer, Luke , urldate =. Spurious Rewards: Rethinking Training Signals in. 2026 , eprinttype =. doi:10.48550/ar...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2506.10947 2026

  15. [15]

    From Reasoning to Super-Intelligence: A Search-Theoretic Perspective

    Shalev-Shwartz, Shai and Shashua, Amnon , urldate =. From Reasoning to Super-Intelligence: A Search-Theoretic Perspective , url =. 2025 , eprinttype =. doi:10.48550/arXiv.2507.15865 , shorttitle =. 2507.15865 [cs] , keywords =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2507.15865 2025

  16. [16]

    Reflexion: Language Agents with Verbal Reinforcement Learning

    Shinn, Noah and Cassano, Federico and Berman, Edward and Gopinath, Ashwin and Narasimhan, Karthik R. and Yao, Shunyu , urldate =. Reflexion: language agents with verbal reinforcement learning , url =. 2023 , langid =. doi:10.48550/arXiv.2303.11366 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2303.11366 2023

  17. [17]

    Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces

    Su,. Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces , url =. 2025 , langid =. doi:10.48550/arXiv.2410.09918 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2410.09918 2025

  18. [18]

    Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing

    Tian, Ye and Peng, Baolin and Song, Linfeng and Jin, Lifeng and Yu, Dian and Han, Lei and Mi, Haitao and Yu, Dong , urldate =. Toward Self-Improvement of. 2024 , langid =. doi:10.48550/arXiv.2404.12253 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2404.12253 2024

  19. [19]

    Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

    Wei, Jason and Wang, Xuezhi and Schuurmans, Dale and Bosma, Maarten and Ichter, Brian and Xia, Fei and Chi, Ed H. and Le, Quoc V. and Zhou, Denny , urldate =. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models , url =. 2023 , langid =. doi:10.48550/arXiv.2201.11903 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2201.11903 2023

  20. [20]

    Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of Language Models

    Yang, Xiao-Wen and Zhu, Xuan-Yi and Wei, Wen-Da and Zhang, Ding-Chu and Shao, Jie-Jing and Zhou, Zhi and Guo, Lan-Zhe and Li, Yu-Feng , urldate =. Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of Language Models , url =. 2025 , eprinttype =. doi:10.48550/arXiv.2502.04404 , shorttitle =. 2502.04404 [cs] , keywords =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2502.04404 2025

  21. [21]

    Tree of Thoughts: Deliberate Problem Solving with Large Language Models

    Yao, Shunyu and Yu, Dian and Zhao, Jeffrey and Shafran, Izhak and Griffiths, Thomas L. and Cao, Yuan and Narasimhan, Karthik R. , urldate =. Tree of Thoughts: Deliberate Problem Solving with Large Language Models , url =. 2023 , langid =. doi:10.48550/arXiv.2305.10601 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2305.10601 2023

  22. [22]

    Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

    Yue, Yang and Chen, Zhiqi and Lu, Rui and Zhao, Andrew and Wang, Zhaokai and Yue, Yang and Song, Shiji and Huang, Gao , urldate =. Does Reinforcement Learning Really Incentivize Reasoning Capacity in. 2025 , langid =. doi:10.48550/arXiv.2504.13837 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2504.13837 2025

  23. [23]

    How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning

    Cai, Hongyi James and Wang, Junlin and Chen, Xiaoyin and Dhingra, Bhuwan , urldate =. How Much Backtracking is Enough? Exploring the Interplay of. 2025 , langid =. doi:10.48550/arXiv.2505.24273 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2505.24273 2025

  24. [24]

    ASTRO: Teaching Language Models to Reason by Reflecting and Backtracking In-Context

    Kim, Joongwon and Goyal, Anirudh and Tan, Liang and Hajishirzi, Hannaneh and Iyer, Srinivasan and Wang, Tianlu , urldate =. 2025 , eprinttype =. doi:10.48550/arXiv.2507.00417 , shorttitle =. 2507.00417 [cs] , keywords =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2507.00417 2025

  25. [25]

    arXiv preprint arXiv:2505.20561 , year=

    Zhang, Shenao and Wang, Yaqing and Liu, Yinxiao and Liu, Tianqi and Grabowski, Peter and Ie, Eugene and Wang, Zhaoran and Li, Yunxuan , urldate =. Beyond Markovian: Reflective Exploration via Bayes-Adaptive. 2025 , langid =. doi:10.48550/arXiv.2505.20561 , eprint =

    work page doi:10.48550/arxiv.2505.20561 2025

  26. [26]

    SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training

    Chu, Tianzhe and Zhai, Yuexiang and Yang, Jihan and Tong, Shengbang and Xie, Saining and Schuurmans, Dale and Le, Quoc V. and Levine, Sergey and Ma, Yi , urldate =. 2025 , langid =. doi:10.48550/arXiv.2501.17161 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2501.17161 2025

  27. [27]

    Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs

    Gandhi, Kanishk and Chakravarthy, Ayush and Singh, Anikait and Lile, Nathan and Goodman, Noah , urldate =. Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective. 2025 , langid =. doi:10.48550/arXiv.2503.01307 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2503.01307 2025

  28. [29]

    PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change

    Valmeekam, Karthik and Olmo, Alberto and Sreedharan, Sarath and Kambhampati, Subbarao , urldate =. Large Language Models Still Can't Plan (A Benchmark for. 2023 , langid =. doi:10.48550/arXiv.2206.10498 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2206.10498 2023

  29. [30]

    LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks

    Kambhampati, Subbarao and Valmeekam, Karthik and Guan, Lin and Verma, Mudit and Stechly, Kaya and Bhambri, Siddhant and Saldyt, Lucas Paul and Murthy, Anil B. , urldate =. 2024 , langid =. doi:10.48550/arXiv.2402.01817 , eprint =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2402.01817 2024

  30. [31]

    Hoffmann, Jörg and Nebel, Bernhard , date =. The

    work page

  31. [32]

    Proceedings of the 16th

    Howey, Richard and Long, Derek and Fox, Maria , urldate =. Proceedings of the 16th. 2004 , file =. doi:10.1109/ICTAI.2004.120 , series =

    work page doi:10.1109/ictai.2004.120 2004