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HFEPX · Eval paper review

KV-streams for Efficient Compaction in Agentic Reinforcement Learning

Emiliano Penaloza, Dane Malenfant, Dheeraj Vattikonda, Roger Creus Castanyer +14 more

Published

Sep 28, 2026

Citations

0

Trust level

Low

Usefulness score

0/100 (Low)

Extraction confidence

20% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Sep 29, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

Background context only.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
0/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory. Context compaction has been the most popular mechanism to alleviate this issue, keeping GPU memory constant for a given trace. Unfortunately, most compaction strategies rely on prefilling the LLM context many times over, hindering training throughput. To alleviate this bottleneck and enable efficient trainable compaction, we propose KV-streams, a plug-and-play strategy compatible with any compaction strategy that substantially increases throughput while showing no evidence of hindering performance. KV-streams enable scalable compaction by streaming the KV cache forward rather than flushing it after each compaction. We show that KV-streams enable three different compaction strategies, achieving a 2.6 to 5x wall-clock speedup in training. Beyond efficiency, we find that the streamed KV cache can act as a recurrent state, carrying forward information that has long since disappeared from the context. Specifically, in a controlled setting we show that, contrary to prior work, RL alone is all that is needed for this behavior to emerge. Overall, we show KV-streams to be an efficient and lightweight plug-and-play addition to any post-training pipeline.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
General
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory.
  • Context compaction has been the most popular mechanism to alleviate this issue, keeping GPU memory constant for a given trace.
  • Unfortunately, most compaction strategies rely on prefilling the LLM context many times over, hindering training throughput.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Contribution summary

  • To alleviate this bottleneck and enable efficient trainable compaction, we propose KV-streams, a plug-and-play strategy compatible with any compaction strategy that substantially increases throughput while showing no evidence of hindering…
  • We show that KV-streams enable three different compaction strategies, achieving a 2.6 to 5x wall-clock speedup in training.
  • Specifically, in a controlled setting we show that, contrary to prior work, RL alone is all that is needed for this behavior to emerge.

Why it matters for eval

  • Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.