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pith:LCZRWHLV

pith:2025:LCZRWHLVQOD7G6MAGDQKCR7PDO

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

Anikait Singh, Ayush Chakravarthy, Kanishk Gandhi, Nathan Lile, Noah D. Goodman

Language models self-improve under RL when they already use reasoning behaviors like verification and backtracking, even if answers start wrong.

arxiv:2503.01307 v2 · 2025-03-03 · cs.CL · cs.LG

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Claims

C1strongest claim

the presence of reasoning behaviors, rather than correctness of answers, proves to be the critical factor -- models primed with incorrect solutions containing proper reasoning patterns achieve comparable performance to those trained on correct solutions.

C2weakest assumption

That the four identified cognitive behaviors are the primary causal drivers of self-improvement differences, and that the controlled priming experiments isolate their effect without confounding influences from model architecture, training history, or unmeasured variables.

C3one line summary

Language models that naturally exhibit verification, backtracking, subgoal setting, and backward chaining improve substantially during RL on verifiable tasks, and these behaviors can be instilled via priming with reasoning-focused examples or filtered pretraining to enable self-improvement.

References

13 extracted · 13 resolved · 2 Pith anchors

[1] REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization 2024 · arXiv:2501.03262

[2] Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters 1971 · doi:10.1007/bf00992696

[3] Backtracking Only: This dataset focuses exclusively on the backtracking strategy, where the model explores solution paths and retreats when encountering dead ends

[4] Backtracking with Answer Verification: In addition to backtracking, this dataset incorporates answer verification, where the model checks its intermediate solutions with the target number

[5] Backtracking with Subgoal Setting: This dataset combines backtracking with explicit subgoal setting, where the model breaks down complex problems into manageable intermediate steps

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Cited by

31 papers in Pith

LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance

Grounded Reinforcement Learning for Visual Reasoning

Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling

Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR

Evaluating the False Trust Engendered by LLM Explanations

Receipt and verification

First computed	2026-05-17T23:38:14.216463Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

58b31b1d758387f3798030e0a147ef1b8ed31dea4ab980832468b55b95f50230

Aliases

arxiv: 2503.01307 · arxiv_version: 2503.01307v2 · doi: 10.48550/arxiv.2503.01307 · pith_short_12: LCZRWHLVQOD7 · pith_short_16: LCZRWHLVQOD7G6MA · pith_short_8: LCZRWHLV

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/LCZRWHLVQOD7G6MAGDQKCR7PDO \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 58b31b1d758387f3798030e0a147ef1b8ed31dea4ab980832468b55b95f50230

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2025-03-03T08:46:22Z",
    "title_canon_sha256": "5c72c80b98692a5792570317ad7d7dd3867bafe3f7650e1d3249552d881d6454"
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