arxiv: 2510.16340 · v2 · submitted 2025-10-18 · 💻 cs.CL · cs.AI

Thinking About Thinking: Evaluating Reasoning in Post-Trained Language Models

Pratham Singla , Shivank Garg , Ayush Singh , Ishan Garg , Ketan Suhaas Saichandran This is my paper

Pith reviewed 2026-05-18 06:47 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords post-trainingreasoning evaluationlanguage modelslatent policiespolicy optimizationSFTDPOGRPO

0 comments p. Extension

The pith

Reinforcement learning post-training makes language models more aware of their learned reasoning policies and better at generalizing them than supervised fine-tuning, but often leaves their internal reasoning traces misaligned with final答案.

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

Post-training techniques now let large language models generate planning tokens for complex logic tasks. The paper asks whether these models understand the policies they have acquired. It defines three competencies: awareness of latent policies, generalization to new but structurally similar tasks, and consistency between reasoning traces and outputs. Experiments compare models trained with supervised fine-tuning against those trained with direct policy optimization and group relative policy optimization across tasks that each demand a distinct policy. Results show reinforcement-learning models outperform supervised fine-tuning on awareness and generalization yet display weaker trace-output alignment, with the gap largest under group relative policy optimization.

Core claim

The paper claims that RL-trained models not only demonstrate greater awareness of their learned behaviors and stronger generalizability to novel, structurally similar tasks than SFT models but also often exhibit weak alignment between their reasoning traces and final outputs, an effect most pronounced in GRPO-trained models.

What carries the argument

Three core competencies: awareness of learned latent policies, generalization of these policies across domains, and alignment between internal reasoning traces and final outputs.

If this is right

RL post-training yields stronger policy awareness than supervised fine-tuning on the tested tasks.
Generalization to structurally similar novel tasks improves under DPO and GRPO relative to SFT.
Trace-output alignment weakens under RL training, reaching its lowest point with GRPO.
Performance gains from RL can therefore coexist with reduced internal consistency in reasoning.

Where Pith is reading between the lines

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

Designers may need new reward signals that explicitly penalize mismatches between traces and answers.
Similar competency tests could be run on models trained with other post-training methods such as preference optimization variants.
If misalignment persists at scale, safety evaluations that rely on generated reasoning may systematically overstate model reliability.

Load-bearing premise

The chosen tasks each require learning a distinct policy and the chosen empirical measures actually capture awareness of latent policies, generalization, and alignment between traces and outputs.

What would settle it

A set of tasks in which GRPO-trained models produce reasoning traces that consistently match their final answers while still showing the claimed gains in awareness and generalization would falsify the reported pattern of misalignment.

Figures

Figures reproduced from arXiv: 2510.16340 by Ayush Singh, Ishan Garg, Ketan Suhaas Saichandran, Pratham Singla, Shivank Garg.

**Figure 2.** Figure 2: Correlations between the model answers and thoughts on three evaluation settings: In-Distribution, [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Figure 3: Results of Sampling Behavior across two evaluation settings: ID and OOD on SFT, DPO, GRPO and [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

read the original abstract

Recent advances in post-training techniques have endowed Large Language Models (LLMs) with enhanced capabilities for tackling complex, logic-intensive tasks through the generation of supplementary planning tokens. This development raises a fundamental question: Are these models aware of what they "learn" and "think"? To address this, we define three core competencies: (1) awareness of learned latent policies, (2) generalization of these policies across domains, and (3) alignment between internal reasoning traces and final outputs. We empirically evaluate these abilities on several tasks, each designed to require learning a distinct policy. Furthermore, we contrast the profiles of models post-trained via Supervised Fine-Tuning (SFT), Direct Policy Optimization (DPO), and Group Relative Policy Optimization (GRPO). Our findings indicate that RL-trained models not only demonstrate greater awareness of their learned behaviors and stronger generalizability to novel, structurally similar tasks than SFT models but also often exhibit weak alignment between their reasoning traces and final outputs, an effect most pronounced in GRPO-trained models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

RL post-training boosts reported awareness and generalization over SFT but the trace-output misalignment finding looks vulnerable to simple performance confounds.

read the letter

The paper's core contribution is a three-part evaluation framework that checks whether post-trained LLMs know what policy they learned, can apply it to new but structurally similar tasks, and keep their reasoning traces consistent with their final answers. They run this on tasks built to require distinct policies and compare SFT against DPO and GRPO. The headline result is that the RL variants score higher on awareness and generalization while showing weaker trace-output alignment, with GRPO worst on the alignment piece. That contrast is the part that feels freshest relative to existing RLHF and interpretability work. The setup is straightforward and the choice to pit SFT directly against two RL methods on the same probes is useful for practitioners who actually ship these models. The abstract is clear about the direction of the differences, which helps. The main weakness is that the awareness metric appears to be measured on the same tasks the models were trained to solve. If the RL models simply solve those tasks more accurately, their higher scores on policy-identification questions could be an artifact of better execution rather than genuine recognition of the latent policy. The stress-test note on this point holds up from the abstract alone; no ablation that holds base accuracy fixed is mentioned. Without seeing the exact task wording, sample sizes, or statistical tests, it is hard to judge how robust the reported gaps are. The generalization claim also rests on the assumption that the new tasks are close enough in structure but not so close that they leak the original policy. This paper is aimed at people who build or evaluate post-training pipelines and want concrete probes for introspection. A reader already working on alignment or controllable generation would find the SFT-versus-RL split worth looking at. It is worth sending to referees because the questions are timely and the empirical contrast is direct, even though the current write-up leaves the independence of the metrics under-specified. Expect the review to focus on controls and task construction rather than the overall framing.

Referee Report

2 major / 1 minor

Summary. The manuscript empirically evaluates three core competencies in post-trained LLMs—awareness of learned latent policies, generalization to novel structurally similar tasks, and alignment between reasoning traces and final outputs—by comparing models post-trained via SFT, DPO, and GRPO on tasks each designed to require a distinct policy. The central finding is that RL-trained models exhibit greater awareness and generalizability than SFT models, but display weak trace-output alignment, most pronounced under GRPO.

Significance. If the metrics can be shown to measure the three competencies independently of raw task performance, the work would offer useful comparative data on how different post-training regimes shape meta-cognitive behaviors in LLMs, with potential implications for interpretability and reliability in reasoning tasks.

major comments (2)

[§3 and empirical evaluation] §3 (definition of the three core competencies) and the empirical evaluation section: Awareness is operationalized via post-training probes (e.g., policy identification questions). No ablation or control is described that holds underlying task accuracy constant across SFT and RL conditions. Without such a control, higher awareness scores in RL models could be an artifact of superior base-task execution rather than genuine recognition of the latent policy.
[Methods / experimental setup] Methods / experimental setup (referenced in abstract): The abstract and visible description provide no details on task construction, sample sizes, statistical controls, or how generalization is quantified across domains. These omissions make it impossible to evaluate whether the reported differences in awareness, generalizability, and alignment are robust or sensitive to post-hoc choices.

minor comments (1)

[Abstract] The abstract would be clearer if it briefly named the specific tasks used to instantiate each distinct policy.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each major comment below and indicate the revisions we plan to incorporate to improve clarity and robustness.

read point-by-point responses

Referee: [§3 and empirical evaluation] §3 (definition of the three core competencies) and the empirical evaluation section: Awareness is operationalized via post-training probes (e.g., policy identification questions). No ablation or control is described that holds underlying task accuracy constant across SFT and RL conditions. Without such a control, higher awareness scores in RL models could be an artifact of superior base-task execution rather than genuine recognition of the latent policy.

Authors: We agree this is a valid concern and that the current results do not fully isolate awareness from base-task performance. In the revised manuscript we will add a controlled ablation that subsamples or matches SFT and RL model variants to comparable levels of task accuracy before computing awareness scores. This will allow us to test whether the reported differences persist when base execution is held constant. revision: yes
Referee: [Methods / experimental setup] Methods / experimental setup (referenced in abstract): The abstract and visible description provide no details on task construction, sample sizes, statistical controls, or how generalization is quantified across domains. These omissions make it impossible to evaluate whether the reported differences in awareness, generalizability, and alignment are robust or sensitive to post-hoc choices.

Authors: The full manuscript already contains task-construction details, sample sizes, and statistical procedures in Sections 4 and 5. To improve accessibility and address sensitivity concerns, we will expand the Methods section with an explicit subsection on generalization quantification (including the precise metric formula) and add a supplementary sensitivity analysis examining robustness to post-hoc parameter choices such as probe thresholds and alignment cutoffs. revision: partial

Circularity Check

0 steps flagged

No circularity: purely empirical evaluation of defined competencies

full rationale

The paper defines three core competencies in the abstract and then reports direct empirical measurements of awareness, generalization, and alignment on policy-learning tasks across SFT, DPO, and GRPO models. No equations, first-principles derivations, or predictions are presented that could reduce to fitted parameters or self-referential definitions. The results are observational comparisons rather than any claimed logical or mathematical chain that loops back to its own inputs by construction, making the work self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work is purely empirical and introduces no free parameters, mathematical axioms, or new invented entities; it relies on the domain assumption that the selected tasks isolate distinct learned policies and that the chosen metrics validly measure awareness, generalization, and trace-output alignment.

axioms (2)

domain assumption The tasks are designed such that each requires learning a distinct policy.
Stated in the abstract when defining the evaluation setup.
domain assumption The empirical measures capture awareness of latent policies, generalization across domains, and alignment between reasoning traces and outputs.
Core premise underlying the three competencies and the reported findings.

pith-pipeline@v0.9.0 · 5720 in / 1394 out tokens · 22685 ms · 2026-05-18T06:47:43.065481+00:00 · methodology

discussion (0)

Reference graph

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