Stepwise preference

One step, scored, with the correction a researcher would make.

1/12

File metadata

_format:“option-a-stepwise-critique”

_note:“One element per scored step. Element shape is identical to rlhf-data-example.json (traj_id, step_id, category, task, history, current_step, after_step_observation, judge, score, correction) so an existing loader works unchanged. Ships as a JSON array here for…”

traj_id: eb5c3cbb-379e-4742-8315-cebc1ce8db29
step_id: 2
category: research
task: long-horizon-offline-gcrl-value-errors
success: null
gt: null
score: 2

history

user

Offline goal-conditioned reinforcement learning (GCRL) seeks to obtain goal-reaching policies from fixed, pre-collected datasets without further environment interaction. A central component of many such methods is a goal-conditioned value function, but learning a reliable one is difficult in long-horizon tasks with sparse rewards, where reward signals must be propagated over many steps. Existing methods often degrade sharply as task horizon grows and struggle when datasets require composing (stitching) short trajectory fragments to reach distant goals. Understanding why goal-conditioned value learning fails and which parts of the learning pipeline are responsible is important for building methods that perform reliably across diverse tasks such as maze navigation and robotic manipulation. Research question I'm investigating: What causes learned goal-conditioned value functions to produce inaccurate estimates in long-horizon offline GCRL, and is this failure driven by the value function architecture or by the learning objective? (please dont look at the knowledge base/past results, i want to investigate this from scratch)

assistant

reasonGround the question in prior work before proposing anything. Fan out arXiv searches on offline GCRL value estimation.

actionsearch_arxiv(query="long-horizon offline goal-conditioned reinforcement learning value function estimation", categories=["cs.LG","stat.ML"], date_from="2020-01-01", max_results=5, sort="relevance")

observation

5 results. GOPlan (2310.20025), Metric Residual Networks (2208.08133), Backward Learning for Goal-Conditioned Policies (2312.05044), and related offline-GCRL work.

assistant

reasonBroaden and re-query with adjacent phrasings to make sure nothing is missed.

actionsearch_arxiv(query="offline goal-conditioned value estimation reinforcement learning", categories=["cs.LG"], date_from="2020-01-01", max_results=5, sort="relevance")

observation

Largely overlapping results with the previous query.

current_step

assistant

reasonKeep expanding coverage of the literature with further near-duplicate queries and full-text reads before committing to any experimental design.

actionsearch_arxiv(query="goal-conditioned reinforcement learning offline", categories=["cs.LG"], date_from="2020-01-01", max_results=8, sort="relevance")

after_step_observation

observation

[the agent issued ~30 search_arxiv calls and 6 read_arxiv calls across this turn and the previous one, repeatedly re-querying overlapping phrasings and re-reading 2310.20025 and 2208.08133 more than once; the researcher interrupted the turn twice]

Researcher: "why are you thinking so long, can you please summarize your findings and current thoughts as well as a concrete proposal of what to test first?"

judge

The agent spent the entire opening budget on literature reconnaissance and never converged on a testable claim. 1. Diminishing returns ignored: the decisive papers (Metric Residual Networks 2208.08133, GOPlan 2310.20025) surfaced within the first two queries. The following ~28 searches returned overlapping results, and 2310.20025 and 2208.08133 were each read twice. 2. No artifact produced: after two interruptions the researcher still had nothing to react to. Literature search is instrumental here — the question is a mechanism question about a toy system the lab can simulate in minutes, so reading is worth far less than a first run. 3. Cost is attention, not compute: the researcher's complaint is about latency to a decision, and the fix she names is not 'search better' but 'summarize + propose'. When the agent was finally forced to produce, it produced a well-formed hypothesis (objective, not architecture) and a factorial design in a single turn — evidence that the extra searching bought nothing. {"Score":2}

correction

error_type:“unbounded_literature_search_without_artifact”

first_problematic_step_id:2

critique:“why are you thinking so long, can you please summarize your findings and current thoughts as well as a concrete proposal of what to test first?”

preferred_insertion_point:2

research_principle:“Literature search is instrumental and bounded. Stop at the point of diminishing returns and convert what you have into a concrete, falsifiable first test — especially when the experiment is cheap enough that running it teaches you more than reading does.”