Papers › Unveiling the Mechanisms of Explicit CoT Training: How CoT Enhances Reasoning Generalization

Unveiling the Mechanisms of Explicit CoT Training: How CoT Enhances Reasoning Generalization

7 Feb 2025arXiv:2502.04667archive 2025-07-28

Xinhao Yao, Ruifeng Ren, Yun Liao, Yong liu

The integration of explicit Chain-of-Thought (CoT) reasoning into training large language models (LLMs) has advanced their reasoning capabilities, yet the mechanisms by which CoT enhances generalization remain poorly understood. This work investigates (1) \textit{how} CoT training reshapes internal model representations and (2) \textit{why} it improves both in-distribution (ID) and out-of-distribution (OOD) reasoning generalization. Through controlled experiments and theoretical analysis, we derive the following key insights. \textbf{1)} Structural Advantage: CoT training internalizes reasoning into a two-stage generalizing circuit, where the number of stages corresponds to the explicit reasoning steps during training. Notably, CoT-trained models resolve intermediate results at shallower layers compared to non-CoT counterparts, freeing up deeper layers to specialize in subsequent reasoning steps. \textbf{2)} Theoretical Analysis: the information-theoretic generalization bounds via distributional divergence can be decomposed into ID and OOD components. While ID error diminishes with sufficient training regardless of CoT, OOD error critically depends on CoT: Non-CoT training fails to generalize to OOD samples due to unseen reasoning patterns, whereas CoT training achieves near-perfect OOD generalization by mastering subtasks and reasoning compositions during training. The identified mechanisms explain our experimental results: CoT training accelerates convergence and enhances generalization from ID to both ID and OOD scenarios while maintaining robust performance even with tolerable noise. These findings are further validated on complex real-world datasets. This paper offers valuable insights for designing CoT strategies to enhance LLM reasoning robustness.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2502.04667")

Code

Syntology Ran 2 of 10 code samples harvested from 1 repository linked to this paper; 8 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · our draft was wrong.

By repository: official repository: 10 samples from 1 repository, 2 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

chen123ctrls/t-cotmechanism officialmentioned in paperjaxMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

10 samples harvested; 2 ran; 1 honoured the contract we drafted; 8 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · honoured contract
1ran · our draft was wrong
8unverified

Licence: 0 of the 10 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from chen123ctrls/t-cotmechanism. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

eval_res chen123ctrls/t-cotmechanism/ControlledExperiments/eval_qa.py official repository ran · honoured contract fingerprinted MIT (permissive) · bedd2bb861320f0e · report
parse_target chen123ctrls/t-cotmechanism/ControlledExperiments/eval_qa.py official repository ran · our draft was wrong MIT (permissive) · 62ef5378e10da132 · report
add_step_noise_to_gsm_fobar chen123ctrls/t-cotmechanism/RealisticDataVerification/data_process.py official repository unverified MIT (permissive) · cabf394868b62573 · report
batch_data chen123ctrls/t-cotmechanism/RealisticDataVerification/utils/gen_vllm.py official repository unverified MIT (permissive) · d49412b3a690206e · report
eval_items chen123ctrls/t-cotmechanism/ControlledExperiments/eval_qa.py official repository unverified MIT (permissive) · a3ae250c1e40eeef · report
get_architectures_from_config_class chen123ctrls/t-cotmechanism/ControlledExperiments/transformers/utils/create_dummy_models.py official repository unverified MIT (permissive) · b176efb26f72e507 · report
get_config_class_from_processor_class chen123ctrls/t-cotmechanism/ControlledExperiments/transformers/utils/create_dummy_models.py official repository unverified MIT (permissive) · 3d999b95009c7537 · report
get_processor_types_from_config_class chen123ctrls/t-cotmechanism/ControlledExperiments/transformers/utils/create_dummy_models.py official repository unverified MIT (permissive) · 0c0b81b081919d8e · report
modify_output chen123ctrls/t-cotmechanism/RealisticDataVerification/data_process.py official repository unverified MIT (permissive) · bc440fba1f01ba1e · report
read_data_source_target chen123ctrls/t-cotmechanism/ControlledExperiments/utils.py official repository unverified MIT (permissive) · aea562a62336c08f · report

Tasks

Generalization BoundsSystematic Generalization

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections