Verifiable Self-Evolution for Open-Ended Dialogue Skills via Future-Feedback Prediction

🕒 Published (v1): 2026-07-21 11:06 UTC · Source: Arxiv · link

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Self-evolution for frozen agents via future-feedback; extends verifiable RL to open dialogue skills

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TL;DR

Open-ended dialogue breaks validation-gated skill evolution because changing a response changes the user's subsequent reaction, making logged feedback an invalid label for counterfactual answers. This paper sidesteps that by evolving a feedback-prediction skill — one that predicts logged user reactions from fixed observed data — and then mining its learned failure criteria to guide answer-skill improvement. The approach achieves >75% accuracy on a balanced proprietary sales-assistant dataset and provides a reproducible offline optimization target without requiring live traffic for every candidate skill.

Problem

Textual skill self-evolution (e.g., SkillOpt) requires a stable held-out validation signal. In verifiable domains (math, code), a changed answer can still be checked against a fixed target. In open-ended dialogue, logged interaction \(x = (C, H, Q_1, A, Q_2)\) cannot validate a counterfactual answer \(A'\): the true utility \(U(A') = \mathbb{E}[Y' \mid X, A']\) depends on an unobserved reaction, so \(p(Y \mid X, A) \neq p(Y \mid X, A')\) in general. Every candidate answer skill thus requires expensive human judgment or live A/B traffic.

Method

Replace the moving optimization target with a verifiable surrogate task: evolve a feedback-prediction skill \(S_F\) that predicts the already-observed binary resolved/unresolved label \(Y\) from the fixed tuple \((X, A)\), i.e., \(\hat{Y} = f_\theta(X, A; S_F)\). The held-out objective

\[J(S_F') = \frac{1}{|D_\text{val}|} \sum_{(X,A,Y)\in D_\text{val}} \mathbb{1}[f_\theta(X, A; S_F') = Y]\]

is measurable without altering any interaction. Validation-gated evolution iteratively proposes bounded textual edits \(\Delta^{(t)}\) to the skill based on high-confidence failures (especially false positives), accepts edits only when \(J\) improves, and rejects otherwise. A lexicographic variant can also optimize asymmetric business costs (e.g., penalizing false-positive satisfaction declarations more than false negatives). The evolved \(S_F\) encodes interpretable criteria distinguishing genuine resolution from superficial completion; these criteria are then transferred conceptually to diagnose and constrain the answer skill \(S_A\).

Key Contributions

• Formalizes the moving-target problem: logged next-turn feedback cannot serve as an unbiased validation label for revised answer skills.
• Proposes future-feedback skill evolution: converts conversational feedback into a fixed, verifiable offline learning target by predicting observed labels rather than generating counterfactual answers.
• Identifies a feedback–generation duality: the failure criteria required for accurate dissatisfaction prediction directly map to generation constraints for the answer skill.
• Industrial case study on a privacy-preserving sales-assistant corpus demonstrating >75% held-out accuracy with careful data curation and balanced label partitioning.

Results

• >75% held-out accuracy on a curated, balanced (≈1:1 resolved/unresolved) proprietary sales-assistant dataset; random/constant-classifier baseline is ≈50% on this construction.
• Two engineering choices were decisive: (1) removing weakly labeled and ambiguous interactions, and (2) constructing balanced partitions to make accuracy directly interpretable.
• Qualitative evidence that evolved rules de-emphasize fluent language or completed tool calls and instead require coverage, specificity, and actionable next steps.
• No separately measured end-to-end improvement of \(S_A\) is reported; rationale-to-guidance transfer is established conceptually but not causally validated.

Limitations

• Counterfactual distribution shift: the predictor is validated on logged answers; optimizing \(S_A\) may generate out-of-distribution responses that exploit predictor weaknesses — \(S_F\) is an observational model, not a counterfactual oracle.
• Silent users excluded: interactions with no interpretable subsequent signal are dropped, introducing selection bias and likely understating silent dissatisfaction.
• Proprietary data: the strongest result depends on private production logs and undisclosed cleaning rules, limiting external reproducibility.
• Binary labels: resolved/unresolved flattens graded or personalized satisfaction; a user may accept a partially satisfactory answer or reject a correct one for out-of-scope reasons.
• No \(S_A\) evaluation: the paper does not report a human preference study or online A/B test of an answer skill derived from the feedback predictor.

Relevance to Agentic AI / LLM Agents

This paper directly addresses a core bottleneck in textual skill self-evolution for LLM agents: without a stable offline signal, iterative skill refinement in open-ended tasks requires expensive live deployment of every candidate. The feedback–generation duality is particularly relevant to agent design, as it shows that a predictor trained for evaluation implicitly encodes actionable generation policies — a reusable knowledge artifact beyond its classification role. The work extends the SkillOpt paradigm to non-verifiable domains, which is the dominant failure mode when deploying agents in real conversational products. Researchers building self-improving dialogue agents or reward-model-free RLHF alternatives will find the formal treatment of the counterfactual validity boundary practically important.