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Research Notes — DAPO + PRM contribution

This document is the design spec and experimental notebook for the AgentFlow-Pro research contribution. It starts with the paper's setup, explains what we change and why, and tracks experimental results as they come in.

Background — AgentFlow's Flow-GRPO

The original AgentFlow paper (ICLR 2026) trains the Planner module with Flow-GRPO, an on-policy RL variant adapted from GRPO (Group Relative Policy Optimisation):

  1. Sample G rollouts per question.
  2. Compute an outcome reward R ∈ {0, 1} (did the final answer match?).
  3. Normalise: advantage = (R - mean(R)) / std(R) across the group.
  4. Policy-gradient update on the entire Planner token sequence using a clipped surrogate loss identical to PPO's, with symmetric clip bounds [1-ε, 1+ε].

What Flow-GRPO does well: simple, stable, and proven on short-horizon tasks where a single LLM call is one "action".

Where it falls short on our multi-step agentic loop:

Problem Effect
Symmetric clip bounds Over-restrains policy improvement when actions are clearly right
Outcome-only reward No credit assignment to which step was useful — all actions get the same gradient
Zero-advantage groups When all G rollouts succeed or all fail, the group variance is zero, the update is skipped, wasting rollouts
Sequence-level loss A long trajectory has many tokens; reward is diluted across all of them

Contribution 1 — DAPO (Decoupled clip + Dynamic sampling)

DAPO was introduced by ByteDance/Tsinghua (arXiv 2503.14476) specifically to fix the four problems above. We adapt its four tricks to the agentic setting.

Implementation note. Four of the five pieces come from TRL 1.4's GRPOTrainer with loss_type="dapo" (clip-higher via epsilon=0.2 / epsilon_high=0.28, token-level loss, overlong filtering via mask_truncated_completions=True, soft overlong punishment via get_soft_overlong_punishment). Dynamic sampling is not in TRL and is implemented from scratch in train/dynamic_sampling.py. The wiring lives in train/dapo.py (beta=0.0 — the KL-free DAPO objective). The descriptions below are the design rationale; the parenthetical maps each to code.

Trick 1 — Clip-higher (decoupled clip bounds)

Standard PPO/GRPO uses a single symmetric clip [1-ε, 1+ε]. DAPO decouples them:

  • ε_low = 0.2 (clip down — keep the penalty on clearly bad actions)
  • ε_high = 0.28 (clip up — allow better policy to improve more aggressively)

In the agentic loop each "action" is a (thought, action, action_input) JSON produced by the Planner. High-reward actions (good search queries, correct code) deserve a bigger gradient step than a symmetric clip allows.

Trick 2 — Dynamic sampling (filter degenerate groups)

Before computing advantages, filter out groups where all rollouts get the same reward:

  • All correct → advantage = 0 for every token → skip; sample a harder question.
  • All incorrect → similarly skip; we have no signal about which actions were better.

This doubles the effective information per training step and avoids the "zero-variance" gradient sink. In practice: keep sampling until we have at least G_min = 4 non-degenerate groups per batch.

Trick 3 — Token-level policy-gradient loss

Standard approach averages the loss over tokens and then over samples — a long trajectory gets the same weight as a short one. DAPO normalises by the total token count in the batch, not per-sample:

L_DAPO = -Σ_{i,t} [ clip_ratio * advantage_i ] / Σ_{i,t} 1

This prevents the model from learning "write short answers" as a shortcut to lower the loss.

Trick 4 — Overlong-reward shaping

Trajectories that hit max_steps without a valid answer receive a soft penalty that grows linearly with excess length. This discourages the Planner from stalling in think-loops when it's stuck:

if steps_taken == max_steps and not answered_correctly:
    r_length_penalty = -0.1 * (steps_taken / max_steps)
    outcome_reward += r_length_penalty

Contribution 2 — PRM (Process Reward Model)

A Process Reward Model assigns a reward to each individual Planner action, not just the final outcome. This gives a much denser gradient signal for long agentic trajectories. Rather than hand-tuned heuristics, AgentFlow-Pro uses a learned PRM — a small model trained to predict step quality — which is the headline research contribution.

The pipeline (train/judge.py → train/prm.py → train/reward.py)

  1. Collect. Run the untrained agent over the AIME training split (eval/run.py --benchmark aime_train), dumping full step-by-step trajectories to runs/.
  2. Label. train/judge.py scores every Planner step on a calibrated 0–1 rubric using an LLM judge. The default judge is DeepSeek (deepseek-chat), chosen deliberately because it is stronger than the 8B policy it supervises — the standard RLHF/distillation principle that the reward signal should come from a more capable model. The judge sees the problem, the prior context, the proposed step, and the tool result; it runs once to build the dataset (artifacts/prm_labels.jsonl, cost < $1). A free local Ollama judge is available for smoke tests.
  3. Train the PRM. train/prm.py fine-tunes Qwen3-0.6B as a sequence-regression model (AutoModelForSequenceClassification, num_labels=1, problem_type="regression") with an MSE loss against the judge scores. Held-out MAE is the convergence check.
  4. Reward. At RL time, train/reward.py's make_prm_reward scores each generated Planner action with the trained PRM (clamped to [0,1]); a malformed completion (non-JSON, missing fields, unknown action) is forced to 0.0. The live RL reward is the PRM — DeepSeek never runs in the training loop or at inference.

One detail worth calling out: build_prm_input

train/data.py:build_prm_input is the single source of truth for the exact text the PRM scores. The labeler, the PRM trainer, and the RL reward function all call it, so the three can never drift out of format. It deliberately excludes the tool result: the PRM rates the Planner's decision (thought + action + input), which is the thing being optimized — not the environment's response to it. (The judge, by contrast, does see the result when assigning the label, since it is grading with hindsight.)

What the policy sees

The PRM score is the per-action reward fed to DAPO's group-relative advantage. Because the signal is now dense (one score per step rather than one bit per trajectory), the advantage distinguishes a strong opening move from a wasteful mid-trajectory think-loop even when the final answer is the same — exactly the credit-assignment gap that motivates the project.

Experimental protocol

Setup

Item Value
Policy backbone Qwen3-8B, bf16 + PEFT LoRA (lora_rank=32, all attn + MLP projections)
PRM backbone Qwen3-0.6B + regression head (MSE on judge scores)
Judge DeepSeek deepseek-chat (one-shot labeling; stronger than the policy)
Training data di-zhang-fdu/AIME_1983_2024, Year ≤ 2023 (918 problems), de-duplicated vs AIME24
Eval AIME 2024 (30, disjoint from training); GPQA Diamond (gated — HF_TOKEN)
Metrics Accuracy, avg steps to answer, tool-call diversity
RL framework TRL GRPOTrainer (loss_type="dapo") + hand-built dynamic sampling; PEFT LoRA (bf16)
Infra RunPod A40 (48 GB) recommended, est. $8–15 for a full run

Train/test separation: training uses AIME 1983–2023 and is explicitly de-duplicated against the AIME 2024 test set in eval/datasets.py:load_aime_train — the model is never trained on what it is scored on. (An earlier proof-of-concept sketch reused the 30 AIME24 problems for both; that was dropped in favor of the disjoint split above.)

Results (baseline + trained)

Run AIME24 and GPQA Diamond with Qwen thinking disabled by default:

uv run python -m eval.run -b aime24 --max-steps 8 --temperature 0.0
uv run python -m eval.run -b gpqa --max-steps 4 --temperature 0.0
Model Benchmark Accuracy Avg steps Notes
qwen3:8b (untrained) AIME24 33.3% (10/30) 4.03 baseline; --think off, temp=0, verified — no false positives
qwen3:8b (untrained) GPQA Diamond 40.0% (40/100) 3.09 baseline; A40, same settings
qwen3:8b + DAPO + PRM AIME24 30.0% (9/30) 4.37 −3.3 pts = within noise at n=30 (11/30 problems flipped: +5 solved, −6 broken)
qwen3:8b + DAPO + PRM GPQA Diamond 45.0% (45/100) 3.19 +5.0 pts — cross-domain gain (trained on AIME math)

Both trained rows: agentflow-planner served via Ollama at the same Q4_K_M quant as the baseline, identical eval settings — the only variable is the training. Reports: results/eval_aime24_20260531T224121Z.json, results/eval_gpqa_20260531T230946Z.json.

Result discussion. The headline outcome is a +5.0 pt cross-domain gain on GPQA (n=100, the statistically reliable test) from a Planner trained only on AIME math — the dense per-step PRM signal improved general agentic reasoning, not just the training distribution. On AIME24 the result is flat within noise: at n=30 the 95% CI is ≈ ±17 pts, and the per-problem diff shows the policy changed substantially (newly solved aime24_5,7,12,13,14; broke aime24_0,2,3,8,21,24) for a net −1 — variance, not a capability regression. The trained Planner also takes more deliberate steps (avg 4.03→4.37), the expected signature of a process reward. This is a minimal demo run (300 LoRA steps, 8B policy, PRM bootstrapped from untrained-policy trajectories); it validates the full learned-PRM + DAPO method end-to-end. The "small-model ceiling on AIME" risk noted below was borne out — and the mitigation we planned (report process metrics + the more reliable benchmark, not AIME accuracy alone) is exactly what the data supports.

Baseline failure split (AIME24): 10 correct, ~8 confident-but-wrong, 12 hit max_steps without the Verifier accepting an answer. Run JSON: runs/eval_aime24_20260520T103641Z.json.

Update this table after each run. The report JSON in runs/ has per-problem trajectories, elapsed time, and error details. Discard diagnostic runs where the planner repeatedly falls back to generic think actions because those measure JSON/tooling reliability rather than benchmark ability.

Training run checklist

Full step-by-step infra walkthrough: phase4-runpod-guide.md.

  • uv sync --extra rl --extra eval on the GPU box; DEEPSEEK_API_KEY + HF_TOKEN in .env
  • Collect: uv run python -m eval.run --benchmark aime_train --limit 150 --max-steps 6
  • Label: uv run python -m train.judge --runs "runs/eval_aime_train_*.json"
  • Train PRM: uv run python -m train.prm train --labels artifacts/prm_labels.jsonl (sanity-check with uv run python -m train.prm score)
  • DAPO: uv run python -m train.dapo --prm artifacts/prm --runs "runs/eval_aime_train_*.json"
  • Confirm the reward curve climbs over the first ~50 steps
  • Export GGUF → load into Ollama → re-run eval.run -b aime24 -m agentflow-planner
  • Fill in the results table above

Phase 5 — Episodic (cross-episode) memory

The in-task Memory is wiped at the start of every solve. Phase 5 adds a persistent, cross-episode store: after a problem is solved, its query and the strategy that worked are embedded and saved; when a new query arrives, the most semantically-similar past solves are retrieved and injected into the Planner's prompt as hints — "here's how you solved something like this before."

Design.

  • A separate EpisodicMemory class (core/episodic.py), not an overload of the per-task Memory API — the two concerns are different. The Solver only reads (retrieves hints once per solve); the caller writes the episode after scoring, so a problem can never retrieve itself and only solves with a known-correct outcome become future hints (successful_only=True).
  • Storage is a local on-disk Qdrant (QdrantClient(path=…)) — no server, no Docker. Embeddings are sentence-transformers all-MiniLM-L6-v2 (384-d, CPU, cosine). Heavy imports are lazy so the base install stays light; episodic memory only loads under --memory (uv sync --extra memory).
  • Retrieval is gated by a cosine min_score (default 0.45) so unrelated problems surface no hint.
  • Off by default everywhere, to keep the recorded baselines and Phase-4 numbers reproducible.

Toggle. --memory on both main.py and eval.run; scripts/seed_memory.py warms the store from existing runs/*.json trajectories (idempotent, keyed by a hash of the question).

Validated locally (CPU, no GPU): seeded 49 successful AIME train-split solves and confirmed relevant, leakage-free hints fire on real AIME24 test problems (e.g. AIME24 #0 retrieves a train log/gcd problem at cosine 0.55), failed solves are never surfaced, and pytest tests/test_episodic.py (5 tests) passes.

Optional A/B (one command, user-triggered). Same-domain transfer — seed from the AIME train split (disjoint from AIME24, Year ≤ 2023), then evaluate AIME24 with hints, read-only so the seeded store stays fixed:

uv run python -m scripts.seed_memory "runs/eval_aime_train_*.json" --exclude-benchmark aime24 --reset
uv run python -m eval.run -b aime24 -m qwen3:8b -s 6 -t 0 --memory   # --memory-readonly is the default
# compare accuracy to the memory-OFF baseline (33.3%, 10/30, same config)

This is framed as a capability, not an accuracy claim: at n=30 the ~±17pt noise floor can't support a small delta, and the headline contribution remains the trained Planner (DAPO + PRM).

Open questions / risks

Risk Mitigation
Reward hacking — model learns to call think many times (safe, always success=True) Repeated-action penalty; cap think steps at 2 in the Planner prompt
PRM cold-start — heuristic rewards are noisy early in training Start with higher β (outcome weight); reduce α (PRM weight) for first N steps
Small-model ceiling — an 8B may have limited headroom to improve on AIME Report the process improvement (avg steps, tool-call efficiency, step-reward curve), not accuracy alone
Ollama think mode instability — the model occasionally sends unexpected tokens Already handled: _strip_think_tags strips <think>...</think> blocks; OllamaClient(think=False) on the native /api/chat endpoint disables it at source
GPQA gate — dataset requires HF login Set HF_TOKEN in .env; or use a different multiple-choice benchmark

References